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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, 04 Dec 2015 11:50:16 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/04/t1449231489mguwtvwkrcr3jtr.htm/, Retrieved Thu, 16 May 2024 18:40:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285101, Retrieved Thu, 16 May 2024 18:40:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [Pearson Correlati...] [2015-12-04 11:50:16] [52853a904b98b07877ca8f5358b56a17] [Current]
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Dataseries X:
164.16
169.17
180.65
168.3
180.73
192.55
159.43
150.11
126.05
106.08
119.92
157.06
156.59
161.21
151.94
137.47
134.1
153.25
166.02
203.24
194.83
208.18
204.4
171.61
180.87
154.12
133.4
139.22
120.43
119.53
90.41
100.48
85.16
70.41
70.04
54.59
59.59
48.84
48.78
47.25
42.9
40.8
43.23
34.23
34.09
38.27
33.9
27.48
31.12
49.16
28.44
26.6
33.02
29.34
27.49
27.67
19.29
17.65
15.43
18.43
22.12
19.88
16.48
14
11.25
17.38
16.45
15.69
15.25
14.64
Dataseries Y:
225.93
250.87
236.24
209.12
237.31
251.67
205.36
167.21
150.42
154.09
183.67
246.66
227.58
214.6
208.41
181.21
185.67
230.86
266.34
310.29
267.18
303.03
278.46
293.36
283.62
274.26
218.12
265.62
226.7
258
196.98
213.38
209.17
184.5
192.61
155.83
176.32
172.13
161.63
163
157.63
154.5
174.28
135.17
141.36
157.83
145.65
112.06
106.85
170.91
156.26
140.78
148.35
132.61
115.72
116.07
98.76
100.33
89.12
88.67
100.58
76.84
81.1
69.6
64.55
80.36
79.79
74.79
64.86
62.27




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285101&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285101&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285101&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean88.8835714285714174.786571428571
Biased Variance4250.705871530614448.41033967347
Biased Standard Deviation65.197437614760766.6964042484561
Covariance3981.54521966874
Correlation0.90254685042261
Determination0.814590817207774
T-Test17.2845704669377
p-value (2 sided)0
p-value (1 sided)0
95% CI of Correlation[0.847254348181219, 0.938490450499834]
Degrees of Freedom68
Number of Observations70

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 88.8835714285714 & 174.786571428571 \tabularnewline
Biased Variance & 4250.70587153061 & 4448.41033967347 \tabularnewline
Biased Standard Deviation & 65.1974376147607 & 66.6964042484561 \tabularnewline
Covariance & 3981.54521966874 \tabularnewline
Correlation & 0.90254685042261 \tabularnewline
Determination & 0.814590817207774 \tabularnewline
T-Test & 17.2845704669377 \tabularnewline
p-value (2 sided) & 0 \tabularnewline
p-value (1 sided) & 0 \tabularnewline
95% CI of Correlation & [0.847254348181219, 0.938490450499834] \tabularnewline
Degrees of Freedom & 68 \tabularnewline
Number of Observations & 70 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285101&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]88.8835714285714[/C][C]174.786571428571[/C][/ROW]
[ROW][C]Biased Variance[/C][C]4250.70587153061[/C][C]4448.41033967347[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]65.1974376147607[/C][C]66.6964042484561[/C][/ROW]
[ROW][C]Covariance[/C][C]3981.54521966874[/C][/ROW]
[ROW][C]Correlation[/C][C]0.90254685042261[/C][/ROW]
[ROW][C]Determination[/C][C]0.814590817207774[/C][/ROW]
[ROW][C]T-Test[/C][C]17.2845704669377[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0[/C][/ROW]
[ROW][C]95% CI of Correlation[/C][C][0.847254348181219, 0.938490450499834][/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]68[/C][/ROW]
[ROW][C]Number of Observations[/C][C]70[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285101&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285101&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
Mean88.8835714285714174.786571428571
Biased Variance4250.705871530614448.41033967347
Biased Standard Deviation65.197437614760766.6964042484561
Covariance3981.54521966874
Correlation0.90254685042261
Determination0.814590817207774
T-Test17.2845704669377
p-value (2 sided)0
p-value (1 sided)0
95% CI of Correlation[0.847254348181219, 0.938490450499834]
Degrees of Freedom68
Number of Observations70







Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 7.6114, p-value = 0.02224
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 2.5941, p-value = 0.2733
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 3.5003, p-value = 7.719e-09
> ad.y
	Anderson-Darling normality test
data:  y
A = 0.44284, p-value = 0.2794

\begin{tabular}{lllllllll}
\hline
Normality Tests \tabularnewline
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 7.6114, p-value = 0.02224
alternative hypothesis: greater
\tabularnewline
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 2.5941, p-value = 0.2733
alternative hypothesis: greater
\tabularnewline
> ad.x
	Anderson-Darling normality test
data:  x
A = 3.5003, p-value = 7.719e-09
\tabularnewline
> ad.y
	Anderson-Darling normality test
data:  y
A = 0.44284, p-value = 0.2794
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=285101&T=2

[TABLE]
[ROW][C]Normality Tests[/C][/ROW]
[ROW][C]
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 7.6114, p-value = 0.02224
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 2.5941, p-value = 0.2733
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> ad.x
	Anderson-Darling normality test
data:  x
A = 3.5003, p-value = 7.719e-09
[/C][/ROW] [ROW][C]
> ad.y
	Anderson-Darling normality test
data:  y
A = 0.44284, p-value = 0.2794
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=285101&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285101&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 = 7.6114, p-value = 0.02224
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 2.5941, p-value = 0.2733
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 3.5003, p-value = 7.719e-09
> ad.y
	Anderson-Darling normality test
data:  y
A = 0.44284, p-value = 0.2794



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$estimate)[1,1]
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistic',1,TRUE)
a<-table.element(a,'Variable X',1,TRUE)
a<-table.element(a,'Variable Y',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('arithmetic_mean.htm','Mean',''),header=TRUE)
a<-table.element(a,mean(x))
a<-table.element(a,mean(y))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('biased.htm','Biased Variance',''),header=TRUE)
a<-table.element(a,varx)
a<-table.element(a,vary)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('biased1.htm','Biased Standard Deviation',''),header=TRUE)
a<-table.element(a,sqrt(varx))
a<-table.element(a,sqrt(vary))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('covariance.htm','Covariance',''),header=TRUE)
a<-table.element(a,cov(x,y),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('pearson_correlation.htm','Correlation',''),header=TRUE)
a<-table.element(a,cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('coeff_of_determination.htm','Determination',''),header=TRUE)
a<-table.element(a,cxy*cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('ttest_statistic.htm','T-Test',''),header=TRUE)
a<-table.element(a,as.matrix(corxy$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,'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')
qq.plot(x,main='QQplot of variable x')
dev.off()
bitmap(file='test3.png')
qq.plot(y,main='QQplot of variable y')
dev.off()