Free Statistics

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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 07:27:30 -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/t122433661019jhyh2dqdnmfw6.htm/, Retrieved Sun, 19 May 2024 15:37:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=16617, Retrieved Sun, 19 May 2024 15:37:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Eerste tijdreeks] [2008-10-11 15:49:53] [c45c87b96bbf32ffc2144fc37d767b2e]
-   PD  [Univariate Data Series] [aantal begonnen w...] [2008-10-18 13:04:32] [c45c87b96bbf32ffc2144fc37d767b2e]
F RMPD      [Pearson Correlation] [relatie tijdreeks...] [2008-10-18 13:27:30] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
F    D        [Pearson Correlation] [relatie tijdreeks...] [2008-10-18 13:31:33] [c45c87b96bbf32ffc2144fc37d767b2e]
F    D          [Pearson Correlation] [relatie tijdreeks...] [2008-10-18 13:35:31] [c45c87b96bbf32ffc2144fc37d767b2e]
F    D            [Pearson Correlation] [relatie tijdreeks...] [2008-10-18 13:37:37] [c45c87b96bbf32ffc2144fc37d767b2e]
F    D              [Pearson Correlation] [relatie tijdreeks...] [2008-10-18 13:39:20] [c45c87b96bbf32ffc2144fc37d767b2e]
F    D                [Pearson Correlation] [relatie tijdreeks...] [2008-10-18 13:41:34] [c45c87b96bbf32ffc2144fc37d767b2e]
Feedback Forum
2008-10-27 16:32:32 [Michael Van Spaandonck] [reply
Een negatieve corelatie van 66% duidt op een meer dan gemiddeld omgekeerd lineair verband tussen de twee datareeksen. Concreet betekent dit dat de bewoonbare oppervlakte per woongelegenheid daalt naarmate er meer woongelegenheden per gebouw zijn.

Post a new message
Dataseries X:
1.83
1.91
1.68
1.82
1.75
1.83
1.73
1.81
2.10
1.90
1.91
2.02
1.99
1.96
1.81
1.75
1.82
2.00
1.91
1.84
1.83
1.94
1.83
1.79
1.98
1.95
1.81
1.79
1.84
1.89
1.78
1.84
2.00
1.87
1.98
2.02
2.10
1.99
1.77
1.83
1.94
1.80
1.92
1.85
2.10
2.03
2.07
2.10
2.14
2.17
1.93
1.87
2.07
1.86
1.82
1.95
2.02
2.01
2.06
2.07
1.86
1.96
1.81
1.84
Dataseries Y:
104.21
103.86
114.42
113.08
110.80
111.22
114.08
111.72
106.40
108.19
106.88
106.60
108.83
104.10
107.03
108.49
104.75
101.04
105.63
105.10
108.15
105.62
102.83
104.81
102.86
102.92
107.68
106.82
103.73
107.33
108.42
111.25
103.60
103.24
101.18
102.61
104.14
101.36
110.10
106.77
103.05
104.56
107.06
108.98
103.26
103.24
100.58
100.73
101.73
99.25
105.24
106.44
105.66
109.59
110.36
109.04
103.89
103.70
107.93
102.38
107.30
105,25
106.72
112.71




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=16617&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=16617&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16617&T=0

As an alternative you can also use a 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
Mean1.91328125106.1015625
Biased Variance0.012743920898437512.0573600585937
Biased Standard Deviation0.1128889759827663.47237095636307
Covariance-0.266606795634921
Correlation-0.66950548901321
Determination0.448237599818817
T-Test-7.09698578956399
p-value (2 sided)1.46068797285892e-09
p-value (1 sided)7.30343986429458e-10
Degrees of Freedom62
Number of Observations64

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 1.91328125 & 106.1015625 \tabularnewline
Biased Variance & 0.0127439208984375 & 12.0573600585937 \tabularnewline
Biased Standard Deviation & 0.112888975982766 & 3.47237095636307 \tabularnewline
Covariance & -0.266606795634921 \tabularnewline
Correlation & -0.66950548901321 \tabularnewline
Determination & 0.448237599818817 \tabularnewline
T-Test & -7.09698578956399 \tabularnewline
p-value (2 sided) & 1.46068797285892e-09 \tabularnewline
p-value (1 sided) & 7.30343986429458e-10 \tabularnewline
Degrees of Freedom & 62 \tabularnewline
Number of Observations & 64 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16617&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]1.91328125[/C][C]106.1015625[/C][/ROW]
[ROW][C]Biased Variance[/C][C]0.0127439208984375[/C][C]12.0573600585937[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]0.112888975982766[/C][C]3.47237095636307[/C][/ROW]
[ROW][C]Covariance[/C][C]-0.266606795634921[/C][/ROW]
[ROW][C]Correlation[/C][C]-0.66950548901321[/C][/ROW]
[ROW][C]Determination[/C][C]0.448237599818817[/C][/ROW]
[ROW][C]T-Test[/C][C]-7.09698578956399[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]1.46068797285892e-09[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]7.30343986429458e-10[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]62[/C][/ROW]
[ROW][C]Number of Observations[/C][C]64[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=16617&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16617&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean1.91328125106.1015625
Biased Variance0.012743920898437512.0573600585937
Biased Standard Deviation0.1128889759827663.47237095636307
Covariance-0.266606795634921
Correlation-0.66950548901321
Determination0.448237599818817
T-Test-7.09698578956399
p-value (2 sided)1.46068797285892e-09
p-value (1 sided)7.30343986429458e-10
Degrees of Freedom62
Number of Observations64



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')