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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, 20 Oct 2008 07:15:37 -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/20/t1224508578da7o2evy2zaawb1.htm/, Retrieved Fri, 17 May 2024 07:53:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17237, Retrieved Fri, 17 May 2024 07:53:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

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] [Q4 Clothing produ...] [2007-10-20 14:33:09] [b731da8b544846036771bbf9bf2f34ce]
F R  D    [Pearson Correlation] [Correlatie produc...] [2008-10-20 13:15:37] [f1a30f1149cef3ef3ef69d586c6c3c1c] [Current]
Feedback Forum
2008-10-26 11:48:35 [Kenny Simons] [reply] 
De correlatie ligt hier dichter bij 0 dan bij 1, dus is er geen correlatie. Als we naar het plot kijken, kunnen we 2 segmenten onderscheiden. 1 voor hoge prijzen en 1 voor lage prijzen. Het zou ideaal zijn dat we deze zouden kunnen scheiden en dan zal er wel een verband zijn tussen de tijdreeksen.
2008-10-27 00:12:36 [Kristof Augustyns] [reply] 
Hier is de correlatie 0.287034985095086 en is dus een pak minder als in de vorige oefeningen.
De samenhang is maar 29%, maar er is nog wel correlatie, ook al is het een lichte stijgende lijn die tussen al de puntjes loopt.
Wat de student zegt over 'productie van kleding niet onderhevig is aan prijswijzigingen' is niet juist want er is wel correlatie en als de prijs wijzigt, zal men dat ook wel voelen aan de productie van kleding.
Er is nog altijd 29% samenhang.
2008-10-27 18:56:04 [Yara Van Overstraeten] [reply] 
Er is hier inderdaad een zeer lage correlatie (28,7%) gevonden tussen de productie van kledij en prijs. Er is dus een zwak verband tussen de productie van kledij en de prijs. Dit verband kan men misschien verklaren doordat wanneer de prijs stijgt, textielbedrijven hun activiteit naar het buitenland (lage loonlanden) verhuizen, en zo op deze manier toch dezelfde productie kunnen behouden.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,20
88,60
94,30
98,30
86,40
80,60
104,10
108,20
93,40
71,90
94,10
94,90
96,40
91,10
84,40
86,40
88,00
75,10
109,70
103,00
82,10
68,00
96,40
94,30
90,00
88,00
76,10
82,50
81,40
66,50
97,20
94,10
80,70
70,50
87,80
89,50
99,60
84,20
75,10
92,00
80,80
73,10
99,80
90,00
83,10
72,40
78,80
87,30
91,00
80,10
73,60
86,40
74,50
71,20
92,40
81,50
85,30
69,90
84,20
90,70
100,30
Dataseries Y:
Download CSV
Histogram 
99,90
99,80
99,80
100,30
99,90
99,90
100,00
100,10
100,10
100,20
100,30
100,60
100,00
100,10
100,20
100,00
100,10
100,10
100,10
100,50
100,50
100,50
96,30
96,30
96,80
96,80
96,90
96,80
96,80
96,80
96,80
97,00
97,00
97,00
96,80
96,90
97,20
97,30
97,30
97,20
97,30
97,30
97,30
97,30
97,30
97,30
98,10
96,80
96,80
96,80
96,80
96,80
96,80
96,80
96,80
96,80
96,80
96,80
96,90
97,10
97,10

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=17237&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=17237&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17237&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
Mean86.893442622950898.111475409836
Biased Variance109.8917602794952.39249126578877
Biased Standard Deviation10.48292708548021.54676800645370
Covariance4.7317431693989
Correlation0.287034985095086
Determination0.0823890826685364
T-Test2.30160903402757
p-value (2 sided)0.0249057450545593
p-value (1 sided)0.0124528725272797
Degrees of Freedom59
Number of Observations61

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 86.8934426229508 & 98.111475409836 \tabularnewline
Biased Variance & 109.891760279495 & 2.39249126578877 \tabularnewline
Biased Standard Deviation & 10.4829270854802 & 1.54676800645370 \tabularnewline
Covariance & 4.7317431693989 \tabularnewline
Correlation & 0.287034985095086 \tabularnewline
Determination & 0.0823890826685364 \tabularnewline
T-Test & 2.30160903402757 \tabularnewline
p-value (2 sided) & 0.0249057450545593 \tabularnewline
p-value (1 sided) & 0.0124528725272797 \tabularnewline
Degrees of Freedom & 59 \tabularnewline
Number of Observations & 61 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17237&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]86.8934426229508[/C][C]98.111475409836[/C][/ROW]
[ROW][C]Biased Variance[/C][C]109.891760279495[/C][C]2.39249126578877[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]10.4829270854802[/C][C]1.54676800645370[/C][/ROW]
[ROW][C]Covariance[/C][C]4.7317431693989[/C][/ROW]
[ROW][C]Correlation[/C][C]0.287034985095086[/C][/ROW]
[ROW][C]Determination[/C][C]0.0823890826685364[/C][/ROW]
[ROW][C]T-Test[/C][C]2.30160903402757[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0.0249057450545593[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0.0124528725272797[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]59[/C][/ROW]
[ROW][C]Number of Observations[/C][C]61[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=17237&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17237&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
Mean86.893442622950898.111475409836
Biased Variance109.8917602794952.39249126578877
Biased Standard Deviation10.48292708548021.54676800645370
Covariance4.7317431693989
Correlation0.287034985095086
Determination0.0823890826685364
T-Test2.30160903402757
p-value (2 sided)0.0249057450545593
p-value (1 sided)0.0124528725272797
Degrees of Freedom59
Number of Observations61


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')