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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 23:13:12 +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/t1286838747uy6mo9xy331ab6f.htm/, Retrieved Tue, 30 Apr 2024 17:04:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=82753, Retrieved Tue, 30 Apr 2024 17:04:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Pearson Correlation] [Connected vs Sepa...] [2010-10-04 07:35:56] [b98453cac15ba1066b407e146608df68]
F         [Pearson Correlation] [Task 7a] [2010-10-11 23:13:12] [6f3869f9d1e39c73f93153f1f7803f84] [Current]
Feedback Forum
2010-10-17 13:05:41 [6f5a430a34dfbeab884e51a2f2a26434] [reply] 
Je post de correcte berekening voor task 7, maar je geeft weer geen antwoord.

Beschrijving van de bivariate density plot:
Elk rondje op representeert een student. Wanneer de bolletjes iets donkerder zijn, betekent dit dat er meerdere studenten deze antwoorden gegeven hebben. Hierdoor wordt is er geen duidelijk beeld, de histogrammen verduidelijken de frequenties wel.
Je kan een positief verband zien in het raster van de bivariate density plot.

Omdat de bivariate density plot niet alles eenduidig weergeeft, verkies ik de bivariate kernel density plot. Hier zie je dat de punten op dezelfde plaatsen gelegen zijn, maar de iso-densitycurven verduidelijken de verschillende concentraties aan.

De lijn die door de bivariate kernel density gaat duidt de correlatie aan, de correlatiemaatstaf kan je ook aflezen uit de tabel. 0.52 wijst erop dat er geen perfecte associatie is. Het laat wel zien dat er een positief verband is.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Dataseries Y:
Download CSV
Histogram 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82753&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]1 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=82753&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82753&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 time1 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean36.248484848484835.0454545454545
Biased Variance19.962497704315920.3039944903581
Biased Standard Deviation4.467941103496764.5059953939566
Covariance10.6208897485493
Correlation0.52595026181494
Determination0.276623677903204
T-Test11.1995211160927
p-value (2 sided)0
p-value (1 sided)0
Degrees of Freedom328
Number of Observations330

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 36.2484848484848 & 35.0454545454545 \tabularnewline
Biased Variance & 19.9624977043159 & 20.3039944903581 \tabularnewline
Biased Standard Deviation & 4.46794110349676 & 4.5059953939566 \tabularnewline
Covariance & 10.6208897485493 \tabularnewline
Correlation & 0.52595026181494 \tabularnewline
Determination & 0.276623677903204 \tabularnewline
T-Test & 11.1995211160927 \tabularnewline
p-value (2 sided) & 0 \tabularnewline
p-value (1 sided) & 0 \tabularnewline
Degrees of Freedom & 328 \tabularnewline
Number of Observations & 330 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82753&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]36.2484848484848[/C][C]35.0454545454545[/C][/ROW]
[ROW][C]Biased Variance[/C][C]19.9624977043159[/C][C]20.3039944903581[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]4.46794110349676[/C][C]4.5059953939566[/C][/ROW]
[ROW][C]Covariance[/C][C]10.6208897485493[/C][/ROW]
[ROW][C]Correlation[/C][C]0.52595026181494[/C][/ROW]
[ROW][C]Determination[/C][C]0.276623677903204[/C][/ROW]
[ROW][C]T-Test[/C][C]11.1995211160927[/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]Degrees of Freedom[/C][C]328[/C][/ROW]
[ROW][C]Number of Observations[/C][C]330[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=82753&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82753&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
Mean36.248484848484835.0454545454545
Biased Variance19.962497704315920.3039944903581
Biased Standard Deviation4.467941103496764.5059953939566
Covariance10.6208897485493
Correlation0.52595026181494
Determination0.276623677903204
T-Test11.1995211160927
p-value (2 sided)0
p-value (1 sided)0
Degrees of Freedom328
Number of Observations330


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')