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 computationSun, 26 Oct 2008 04:45:38 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/26/t12250179945jii8e1ryj07csg.htm/, Retrieved Sun, 19 May 2024 14:59:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18843, Retrieved Sun, 19 May 2024 14:59:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Explorative Data Analysis] [Investigation Dis...] [2007-10-21 17:06:37] [b9964c45117f7aac638ab9056d451faa]
F RMPD    [Pearson Correlation] [Eerste bevinding] [2008-10-26 10:45:38] [96c9291ce335a5c9abba7b920811c2df] [Current]
Feedback Forum
2008-11-02 16:56:11 [Kevin Engels] [reply] 
De student mist hier de juiste techniek. Via de lagplot kunnen we wel gaan kijken naar de auto-correlatie. Je kan bij 'lags' dan het aantal lags invoeren, ofwel 12 ofwel maximaal 36. http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/02/t1225624995axnhpl8dtcdlwqe.htm

Bij de eerste lagplot zien we dat de lijn bijna horizontaal loopt en de punten liggen gespreid rond de rechte, dit betekent dat de correlatie bijna 0 is.

Bij lagplot (k=12) zien we dat de punten veel dichter bij de lijn liggen als bij de eerste lag plot, het is een positieve recht wat positieve auto-correlatie met zich meebrengt of seizonale correlatie => je kan zo de productie voor het volgende jaar voorspellen aangezien het telkens de zelfde maanden zijn die een hoge productie afleveren.

Als je nu het aantal lags in 36 veranderd, zie je dit duidelijker opde autocorrelation function, je vindt op maand 12, 24 en 36 telkens een grotere auto-correlatie terug.
http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/02/t1225627086hzsqgveszgl9k18.htm
Conclusie: de tijdreeks is niet random want ze bevat auto-correlatie (seizonale correlatie)
2008-11-03 19:55:14 [Jan Helsen] [reply] 
Ik ga akkoord met bovenstaande conclusie. Je gaat hier je totale tijdreeksen testen op auto correlatie. Deze techniek is niet nauwkeurig wanneer er bijvoorbeeld elk maand maart een piek is. Er is dan wel degelijk sprake van auto correlatie maar wanneer je alles informatie samenvoegt zal deze verdwijnen in het geheel. Deze techniek verdoezeld in feite je auto correlatie.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
80
60
10
20
30
Dataseries Y:
Download CSV
Histogram 
20
40
30
50
60

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18843&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18843&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18843&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'George Udny Yule' @ 72.249.76.132






Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean4040
Biased Variance680200
Biased Standard Deviation26.076809620810614.1421356237310
Covariance-225
Correlation-0.488093530091976
Determination0.238235294117647
T-Test-0.968619604501136
p-value (2 sided)0.404182538276366
p-value (1 sided)0.202091269138183
Degrees of Freedom3
Number of Observations5

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 40 & 40 \tabularnewline
Biased Variance & 680 & 200 \tabularnewline
Biased Standard Deviation & 26.0768096208106 & 14.1421356237310 \tabularnewline
Covariance & -225 \tabularnewline
Correlation & -0.488093530091976 \tabularnewline
Determination & 0.238235294117647 \tabularnewline
T-Test & -0.968619604501136 \tabularnewline
p-value (2 sided) & 0.404182538276366 \tabularnewline
p-value (1 sided) & 0.202091269138183 \tabularnewline
Degrees of Freedom & 3 \tabularnewline
Number of Observations & 5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18843&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]40[/C][C]40[/C][/ROW]
[ROW][C]Biased Variance[/C][C]680[/C][C]200[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]26.0768096208106[/C][C]14.1421356237310[/C][/ROW]
[ROW][C]Covariance[/C][C]-225[/C][/ROW]
[ROW][C]Correlation[/C][C]-0.488093530091976[/C][/ROW]
[ROW][C]Determination[/C][C]0.238235294117647[/C][/ROW]
[ROW][C]T-Test[/C][C]-0.968619604501136[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0.404182538276366[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0.202091269138183[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]3[/C][/ROW]
[ROW][C]Number of Observations[/C][C]5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18843&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18843&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
Mean4040
Biased Variance680200
Biased Standard Deviation26.076809620810614.1421356237310
Covariance-225
Correlation-0.488093530091976
Determination0.238235294117647
T-Test-0.968619604501136
p-value (2 sided)0.404182538276366
p-value (1 sided)0.202091269138183
Degrees of Freedom3
Number of Observations5


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
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')