FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationMon, 11 Oct 2010 23:38:33 +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/2010/Oct/12/t1286840257x7jhrk5xlk8wrka.htm/, Retrieved Tue, 30 Apr 2024 19:26:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=82756, Retrieved Tue, 30 Apr 2024 19:26:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Pearson Correlation] [Connected vs Sepa...] [2010-10-04 07:34:27] [b98453cac15ba1066b407e146608df68]
F         [Pearson Correlation] [Task 8a] [2010-10-11 23:38:33] [6f3869f9d1e39c73f93153f1f7803f84] [Current]
Feedback Forum
2010-10-17 13:10:03 [6f5a430a34dfbeab884e51a2f2a26434] [reply] 
Je geeft ook hier geen antwoord op de vraag.

De algemene redenen waarom ik de bivariate kernel density verkies staan bij vraag 7a. Een extra reden is dat men bij deze berekeningen twee clusters kunnen vaststellen (dit zie je aan de hoogtelijnen).

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41
39
30
31
34
35
39
34
36
37
38
36
38
39
33
32
36
38
39
32
32
31
39
37
39
41
36
33
33
34
31
27
37
34
34
32
29
36
29
35
37
34
38
35
38
37
38
33
36
38
32
32
32
34
32
37
39
29
37
35
30
38
34
31
34
35
36
30
39
35
38
31
34
38
34
39
37
34
28
37
33
37
35
37
32
33
38
33
29
33
31
36
35
32
29
39
37
35
37
32
38
37
36
32
33
40
38
41
36
43
Dataseries Y:
Download CSV
Histogram 
38
32
35
33
37
29
31
36
35
38
31
34
35
38
37
33
32
38
38
32
33
31
38
39
32
32
35
37
33
33
28
32
31
37
30
33
31
33
31
33
32
33
32
33
28
35
39
34
38
32
38
30
33
38
32
32
34
34
36
34
28
34
35
35
31
37
35
27
40
37
36
38
39
41
27
30
37
31
31
27
36
38
37
33
34
31
39
34
32
33
36
32
41
28
30
36
35
31
34
36
36
35
37
28
39
32
35
39
35
42

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82756&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=82756&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82756&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean34.981818181818234.1
Biased Variance10.545123966942111.1990909090909
Biased Standard Deviation3.247325663825873.34650428194719
Covariance3.78165137614679
Correlation0.344824466097388
Determination0.118903912419348
T-Test3.81766937230013
p-value (2 sided)0.000225093161541068
p-value (1 sided)0.000112546580770534
Degrees of Freedom108
Number of Observations110

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 34.9818181818182 & 34.1 \tabularnewline
Biased Variance & 10.5451239669421 & 11.1990909090909 \tabularnewline
Biased Standard Deviation & 3.24732566382587 & 3.34650428194719 \tabularnewline
Covariance & 3.78165137614679 \tabularnewline
Correlation & 0.344824466097388 \tabularnewline
Determination & 0.118903912419348 \tabularnewline
T-Test & 3.81766937230013 \tabularnewline
p-value (2 sided) & 0.000225093161541068 \tabularnewline
p-value (1 sided) & 0.000112546580770534 \tabularnewline
Degrees of Freedom & 108 \tabularnewline
Number of Observations & 110 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82756&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]34.9818181818182[/C][C]34.1[/C][/ROW]
[ROW][C]Biased Variance[/C][C]10.5451239669421[/C][C]11.1990909090909[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]3.24732566382587[/C][C]3.34650428194719[/C][/ROW]
[ROW][C]Covariance[/C][C]3.78165137614679[/C][/ROW]
[ROW][C]Correlation[/C][C]0.344824466097388[/C][/ROW]
[ROW][C]Determination[/C][C]0.118903912419348[/C][/ROW]
[ROW][C]T-Test[/C][C]3.81766937230013[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0.000225093161541068[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0.000112546580770534[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]108[/C][/ROW]
[ROW][C]Number of Observations[/C][C]110[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=82756&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82756&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
Mean34.981818181818234.1
Biased Variance10.545123966942111.1990909090909
Biased Standard Deviation3.247325663825873.34650428194719
Covariance3.78165137614679
Correlation0.344824466097388
Determination0.118903912419348
T-Test3.81766937230013
p-value (2 sided)0.000225093161541068
p-value (1 sided)0.000112546580770534
Degrees of Freedom108
Number of Observations110


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')