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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, 16 Dec 2016 13:17:32 +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/16/t1481890687djfbhxzw47kubur.htm/, Retrieved Thu, 02 May 2024 14:48:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300206, Retrieved Thu, 02 May 2024 14:48:10 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [Pearson] [2016-12-16 12:17:32] [9fb47d69755d1f4b66b6f2591280f9e0] [Current]
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Dataseries X:
10
13
14
NA
NA
13
NA
NA
NA
14
14
12
12
11
12
14
NA
NA
NA
NA
13
NA
13
NA
NA
NA
12
12
13
13
10
12
13
NA
10
14
NA
10
10
14
NA
14
10
13
12
12
NA
12
10
NA
14
NA
NA
8
11
10
NA
14
12
NA
14
13
13
13
12
10
14
11
10
13
NA
NA
NA
NA
NA
12
13
11
10
14
NA
7
NA
13
NA
15
13
14
NA
13
11
NA
14
NA
14
NA
12
13
14
13
NA
NA
12
10
NA
NA
NA
NA
NA
12
NA
NA
NA
9
NA
12
13
NA
13
11
12
11
NA
12
NA
12
13
NA
NA
NA
8
NA
13
NA
8
NA
13
NA
12
15
14
NA
11
NA
10
14
10
15
11
NA
NA
12
13
12
9
NA
14
NA
NA
14
12
15
11
NA
NA
NA
12
NA
11
Dataseries Y:
18
21
21
18
21
21
17
19
23
23
21
20
20
19
19
20
20
NA
21
21
19
22
21
20
21
19
20
20
20
21
19
23
22
NA
18
21
19
19
19
20
21
23
18
23
20
20
20
18
21
NA
21
23
22
21
19
20
17
18
20
21
24
20
22
17
22
18
19
20
20
20
19
23
NA
20
22
19
21
20
19
19
21
18
21
22
22
21
23
22
20
NA
17
19
21
21
21
20
21
22
23
23
20
22
21
22
NA
24
NA
18
20
20
19
20
20
19
19
19
19
20
23
19
19
19
20
21
21
19
19
21
18
18
15
20
21
17
18
19
23
NA
21
24
23
20
20
20
20
22
22
21
19
20
17
20
19
21
20
21
22
19
17
21
20
19
22
21
20
20
21
21
19




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=300206&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=300206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300206&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
Mean12.127450980392220.3235294117647
Biased Variance2.895520953479432.70905420991926
Biased Standard Deviation1.701623035069591.64592047496811
Covariance1.39400116482236
Correlation0.492847019881576
Determination0.24289818500615
T-Test5.6641531979683
p-value (2 sided)1.42382353885832e-07
p-value (1 sided)7.11911769429158e-08
95% CI of Correlation[0.330001577392972, 0.627206452903519]
Degrees of Freedom100
Number of Observations102

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 12.1274509803922 & 20.3235294117647 \tabularnewline
Biased Variance & 2.89552095347943 & 2.70905420991926 \tabularnewline
Biased Standard Deviation & 1.70162303506959 & 1.64592047496811 \tabularnewline
Covariance & 1.39400116482236 \tabularnewline
Correlation & 0.492847019881576 \tabularnewline
Determination & 0.24289818500615 \tabularnewline
T-Test & 5.6641531979683 \tabularnewline
p-value (2 sided) & 1.42382353885832e-07 \tabularnewline
p-value (1 sided) & 7.11911769429158e-08 \tabularnewline
95% CI of Correlation & [0.330001577392972, 0.627206452903519] \tabularnewline
Degrees of Freedom & 100 \tabularnewline
Number of Observations & 102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300206&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]12.1274509803922[/C][C]20.3235294117647[/C][/ROW]
[ROW][C]Biased Variance[/C][C]2.89552095347943[/C][C]2.70905420991926[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]1.70162303506959[/C][C]1.64592047496811[/C][/ROW]
[ROW][C]Covariance[/C][C]1.39400116482236[/C][/ROW]
[ROW][C]Correlation[/C][C]0.492847019881576[/C][/ROW]
[ROW][C]Determination[/C][C]0.24289818500615[/C][/ROW]
[ROW][C]T-Test[/C][C]5.6641531979683[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]1.42382353885832e-07[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]7.11911769429158e-08[/C][/ROW]
[ROW][C]95% CI of Correlation[/C][C][0.330001577392972, 0.627206452903519][/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]100[/C][/ROW]
[ROW][C]Number of Observations[/C][C]102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300206&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300206&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
Mean12.127450980392220.3235294117647
Biased Variance2.895520953479432.70905420991926
Biased Standard Deviation1.701623035069591.64592047496811
Covariance1.39400116482236
Correlation0.492847019881576
Determination0.24289818500615
T-Test5.6641531979683
p-value (2 sided)1.42382353885832e-07
p-value (1 sided)7.11911769429158e-08
95% CI of Correlation[0.330001577392972, 0.627206452903519]
Degrees of Freedom100
Number of Observations102







Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 6.7302, p-value = 0.03456
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 0.11092, p-value = 0.9461
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 2.4197, p-value = 3.674e-06
> ad.y
	Anderson-Darling normality test
data:  y
A = 1.7926, p-value = 0.0001288

\begin{tabular}{lllllllll}
\hline
Normality Tests \tabularnewline
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 6.7302, p-value = 0.03456
alternative hypothesis: greater
\tabularnewline
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 0.11092, p-value = 0.9461
alternative hypothesis: greater
\tabularnewline
> ad.x
	Anderson-Darling normality test
data:  x
A = 2.4197, p-value = 3.674e-06
\tabularnewline
> ad.y
	Anderson-Darling normality test
data:  y
A = 1.7926, p-value = 0.0001288
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=300206&T=2

[TABLE]
[ROW][C]Normality Tests[/C][/ROW]
[ROW][C]
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 6.7302, p-value = 0.03456
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 0.11092, p-value = 0.9461
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> ad.x
	Anderson-Darling normality test
data:  x
A = 2.4197, p-value = 3.674e-06
[/C][/ROW] [ROW][C]
> ad.y
	Anderson-Darling normality test
data:  y
A = 1.7926, p-value = 0.0001288
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300206&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300206&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 = 6.7302, p-value = 0.03456
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 0.11092, p-value = 0.9461
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 2.4197, p-value = 3.674e-06
> ad.y
	Anderson-Darling normality test
data:  y
A = 1.7926, p-value = 0.0001288



Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 4 ; par6 = White Noise ; par7 = 0.95 ;
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()