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 computationSat, 18 Oct 2008 04:39:45 -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/18/t1224326521dncl33x5yd1z2un.htm/, Retrieved Wed, 22 May 2024 09:07:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=16566, Retrieved Wed, 22 May 2024 09:07:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Back to Back Histogram] [Q8 - 1 2 ] [2008-10-18 09:47:41] [a0d819c22534897f04a2f0b92f1eb36a]
F   PD  [Back to Back Histogram] [Q8 - 1 3 ] [2008-10-18 09:53:12] [a0d819c22534897f04a2f0b92f1eb36a]
F RMPD      [Pearson Correlation] [Q10 - 1 2] [2008-10-18 10:39:45] [5f3e73ccf1ddc75508eed47fa51813d3] [Current]
Feedback Forum
2008-10-27 16:55:34 [Bernard Femont] [reply] 
Er is een groot verband tussen deze 2 grafieken aangezien de correlatie 85% bedraagt. Hier is een vrij logische verklaring voor. Wanneer er bv. een procentuele stijging is van de totale uitvoer, zal er een stijging zijn van de uitvoer Intra en Extra-EU. Kijk bv. naar productie: wanneer er meer geproduceerd wordt zal er ook meer van verkocht worden, ongeacht binnen of buitenland. Bij deze grafiek is dit juist hetzelfde
2008-10-27 19:07:57 [Nathalie Boden] [reply] 
We zien hier dat er een groot verband is tussen de beide gegevens namelijk een percentage van 85% Zo gaan we zien dat wanneer de invoer extra eu en de uitvoer extra eu een andere waarde gaat aannemen en ze invloed op elkaar gaan uitoefenen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12112
10875,2
9897,3
11672,1
12385,7
11405,6
9830,9
11025,1
10853,8
12252,6
11839,4
11669,1
11601,4
11178,4
9516,4
12102,8
12989
11610,2
10205,5
11356,2
11307,1
12648,6
11947,2
11714,1
12192,5
11268,8
9097,4
12639,8
13040,1
11687,3
11191,7
11391,9
11793,1
13933,2
12778,1
11810,3
13698,4
11956,6
10723,8
13938,9
13979,8
13807,4
12973,9
12509,8
12934,1
14908,3
13772,1
13012,6
14049,9
11816,5
11593,2
14466,2
13615,9
14733,9
13880,7
13527,5
13584
16170,2
13260,6
14741,9
15486,5
13154,5
12621,2
15031,6
15452,4
15428
13105,9
14716,8
14180
16202,2
14392,4
15140,6
15960,1
14351,3
13230,2
15202,1
17157,3
16159,1
13405,7
17224,7
17338,4
17370,6
18817,8
16593,2
17979,5
Dataseries Y:
Download CSV
Histogram 
3532,8
3693,1
2622,9
3130,8
3487,5
3349,7
3044,2
3266
3351,5
3606,8
3419,5
3829,5
3505,1
3845,3
2566,6
3658,5
3954
3460,1
3454,1
3412,8
3418
3349,5
3423,4
3242,8
3277,2
3833
2606,3
3643,8
3686,4
3281,6
3669,3
3191,5
3512,7
3970,7
3601,2
3610
4172,1
3956,2
3142,7
3884,3
3892,2
3613
3730,5
3481,3
3649,5
4215,2
4066,6
4196,8
4536,6
4441,6
3548,3
4735,9
4130,6
4356,2
4159,6
3988
4167,8
4902,2
3909,4
4697,6
4308,9
4420,4
3544,2
4433
4479,7
4533,2
4237,5
4207,4
4394
5148,4
4202,2
4682,5
4884,3
5288,9
4505,2
4611,5
5081,1
4523,1
4412,8
4647,4
4778,6
4495,3
4633,5
4360,5
4517,9

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16566&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=16566&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16566&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'Gwilym Jenkins' @ 72.249.127.135






Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean13249.15529411763934.28117647059
Biased Variance4181281.63282491347233.207880969
Biased Standard Deviation2044.81823955698589.264972555614
Covariance1031756.62105322
Correlation0.84619857231465
Determination0.716052023787353
T-Test14.4674485180386
p-value (2 sided)0
p-value (1 sided)0
Degrees of Freedom83
Number of Observations85

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 13249.1552941176 & 3934.28117647059 \tabularnewline
Biased Variance & 4181281.63282491 & 347233.207880969 \tabularnewline
Biased Standard Deviation & 2044.81823955698 & 589.264972555614 \tabularnewline
Covariance & 1031756.62105322 \tabularnewline
Correlation & 0.84619857231465 \tabularnewline
Determination & 0.716052023787353 \tabularnewline
T-Test & 14.4674485180386 \tabularnewline
p-value (2 sided) & 0 \tabularnewline
p-value (1 sided) & 0 \tabularnewline
Degrees of Freedom & 83 \tabularnewline
Number of Observations & 85 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16566&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]13249.1552941176[/C][C]3934.28117647059[/C][/ROW]
[ROW][C]Biased Variance[/C][C]4181281.63282491[/C][C]347233.207880969[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]2044.81823955698[/C][C]589.264972555614[/C][/ROW]
[ROW][C]Covariance[/C][C]1031756.62105322[/C][/ROW]
[ROW][C]Correlation[/C][C]0.84619857231465[/C][/ROW]
[ROW][C]Determination[/C][C]0.716052023787353[/C][/ROW]
[ROW][C]T-Test[/C][C]14.4674485180386[/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]83[/C][/ROW]
[ROW][C]Number of Observations[/C][C]85[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=16566&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16566&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
Mean13249.15529411763934.28117647059
Biased Variance4181281.63282491347233.207880969
Biased Standard Deviation2044.81823955698589.264972555614
Covariance1031756.62105322
Correlation0.84619857231465
Determination0.716052023787353
T-Test14.4674485180386
p-value (2 sided)0
p-value (1 sided)0
Degrees of Freedom83
Number of Observations85


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')