Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_partialcorrelation.wasp
Title produced by softwarePartial Correlation
Date of computationTue, 11 Nov 2008 10:31:20 -0700
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/Nov/11/t1226424734x2bfa6m4mcmap9l.htm/, Retrieved Sun, 19 May 2024 10:47:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23759, Retrieved Sun, 19 May 2024 10:47:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Partial Correlation] [Partial correlation] [2008-11-04 19:15:33] [077ffec662d24c06be4c491541a44245]
F   P   [Partial Correlation] [Partial correlation] [2008-11-11 16:11:52] [73d6180dc45497329efd1b6934a84aba]
F   P       [Partial Correlation] [Partial Correlation] [2008-11-11 17:31:20] [14a75ec03b2c0d8ddd8b141a7b1594fd] [Current]
F             [Partial Correlation] [] [2008-11-11 19:37:29] [a7a7b7de998247cdf0f65ef79d563d66]
F RMP         [Trivariate Scatterplots] [] [2008-11-11 19:40:32] [a7a7b7de998247cdf0f65ef79d563d66]
-             [Partial Correlation] [] [2008-11-21 17:12:08] [888addc516c3b812dd7be4bd54caa358]
Feedback Forum
2008-11-20 23:20:21 [Olivier Uyttendaele] [reply
Dit is voor een gedeelte juist, je berekent inderdaad de associatie van 3 variabelen maar je gaat ook opzoek naar de partiele correlatie.
De correlatie van de 2 variabelen X & Y van hierboven (bivariate density) noem je bijvoorbeeld de simpele correlatie r(X,Y). Bij dit model introduceer je dan een 3de variabele Z. Bedoeling hier is om te bekijken of Z -misschien wel of misschien niet- een invloed heeft de relatie tussen X & Y.

Via de partial correlation te berekenen tussen X & Y kan je dus nagaan of Z een factor is die invloed heeft Dit wordt dan r(X,Y|Z). Als r(X,Y) relatief groot is, en r(X,Y|Z) is veel kleiner, dan kan je veronderstellen dat Z een invloedrijke variabele is. Z kan dus voor een gedeelte de relatie uitleggen tussen X & Y. Het zal de relatie uitleggen maar we zullen niet te weten komen wat de relatie veroorzaakt.

Als er 3 variabelen zijn, kan je dus drie eenvoudige correlaties maken nl. r(X,Y), r(X,Z) en r(Y,Z). Wanneer je deze drie correlaties kent, kan je gemakkelijk de partiele correlatie berekenen. Vb; r (X,Z|Y).
Je zal dus telkens op deze manier bij deze de banden met de variabele wegwissen. Wanneer de partial correlation r(X,Y|Z) dicht bij de simpele correlatie ligt, dan kan gesteld worden dat Z weinig invloed heeft op de correlatie tussen X,Y.
2008-11-22 10:45:08 [Kenny Simons] [reply
Deze vraag heb ik grotendeels verkeerd geïnterpreteerd. Met deze techniek ga je op zoek naar de partiële correlatie tussen 3 variabelen. Je moet uitzoeken of de derde variabele (Z), de relatie van de eerste 2 variabelen (X, Y) al dan niet beïnvloedt.

Als de relatie tussen X,Y relatief groot is, en de partiële relatie tussen X,Y en Z is veel kleiner, dan kan je veronderstellen dat Z de relatie tussen de Xen Y beïvnloedt. Z kan dus voor een gedeelte de relatie uitleggen tussen X & Y. Het zal de relatie uitleggen maar we zullen niet te weten komen wat de relatie veroorzaakt. Als de partiële correlatie dicht bij de gewone correlatie ligt, kan je stellen dat de Z-variabele geen vertekend beeld geeft tussen de relatie X en Y.

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Dataseries X:
12300.00
12092.80
12380.80
12196.90
9455.00
13168.00
13427.90
11980.50
11884.80
11691.70
12233.80
14341.40
13130.70
12421.10
14285.80
12864.60
11160.20
14316.20
14388.70
14013.90
13419.00
12769.60
13315.50
15332.90
14243.00
13824.40
14962.90
13202.90
12199.00
15508.90
14199.80
15169.60
14058.00
13786.20
14147.90
16541.70
13587.50
15582.40
15802.80
14130.50
12923.20
15612.20
16033.70
16036.60
14037.80
15330.60
15038.30
17401.80
14992.50
16043.70
16929.60
15921.30
14417.20
15961.00
17851.90
16483.90
14215.50
17429.70
17839.50
17629.20
Dataseries Y:
3423.40
3242.80
3277.20
3833.00
2606.30
3643.80
3686.40
3281.60
3669.30
3191.50
3512.70
3970.70
3601.20
3610.00
4172.10
3956.20
3142.70
3884.30
3892.20
3613.00
3730.50
3481.30
3649.50
4215.20
4066.60
4196.80
4536.60
4441.60
3548.30
4735.90
4130.60
4356.20
4159.60
3988.00
4167.80
4902.20
3909.40
4697.60
4308.90
4420.40
3544.20
4433.00
4479.70
4533.20
4237.50
4207.40
4394.00
5148.40
4202.20
4682.50
4884.30
5288.90
4505.20
4611.50
5081.10
4523.10
4412.80
4647.40
4778.60
4495.30
Dataseries Z:
15370.60
14956.90
15469.70
15101.80
11703.70
16283.60
16726.50
14968.90
14861.00
14583.30
15305.80
17903.90
16379.40
15420.30
17870.50
15912.80
13866.50
17823.20
17872.00
17420.40
16704.40
15991.20
16583.60
19123.50
17838.70
17209.40
18586.50
16258.10
15141.60
19202.10
17746.50
19090.10
18040.30
17515.50
17751.80
21072.40
17170.00
19439.50
19795.40
17574.90
16165.40
19464.60
19932.10
19961.20
17343.40
18924.20
18574.10
21350.60
18594.60
19823.10
20844.40
19640.20
17735.40
19813.60
22238.50
20682.20
17818.60
21872.10
22117.00
21865.90




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23759&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]0 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=23759&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23759&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 time0 seconds
R Server'George Udny Yule' @ 72.249.76.132







Pearson Product Moment Partial Correlation - Ungrouped Data
StatisticValue
Correlation r(xy)0.902863979754093
Partial Correlation r(xy.z)0.283888723977228
Correlation r(xz)0.997734994643543
Partial Correlation r(xz.y)0.988682211673432
Correlation r(yz)0.89643119900075
Partial Correlation r(yz.x)-0.151721858663955

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Partial Correlation - Ungrouped Data \tabularnewline
Statistic & Value \tabularnewline
Correlation r(xy) & 0.902863979754093 \tabularnewline
Partial Correlation r(xy.z) & 0.283888723977228 \tabularnewline
Correlation r(xz) & 0.997734994643543 \tabularnewline
Partial Correlation r(xz.y) & 0.988682211673432 \tabularnewline
Correlation r(yz) & 0.89643119900075 \tabularnewline
Partial Correlation r(yz.x) & -0.151721858663955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23759&T=1

[TABLE]
[ROW][C]Pearson Product Moment Partial Correlation - Ungrouped Data[/C][/ROW]
[ROW][C]Statistic[/C][C]Value[/C][/ROW]
[ROW][C]Correlation r(xy)[/C][C]0.902863979754093[/C][/ROW]
[ROW][C]Partial Correlation r(xy.z)[/C][C]0.283888723977228[/C][/ROW]
[ROW][C]Correlation r(xz)[/C][C]0.997734994643543[/C][/ROW]
[ROW][C]Partial Correlation r(xz.y)[/C][C]0.988682211673432[/C][/ROW]
[ROW][C]Correlation r(yz)[/C][C]0.89643119900075[/C][/ROW]
[ROW][C]Partial Correlation r(yz.x)[/C][C]-0.151721858663955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23759&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23759&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 Partial Correlation - Ungrouped Data
StatisticValue
Correlation r(xy)0.902863979754093
Partial Correlation r(xy.z)0.283888723977228
Correlation r(xz)0.997734994643543
Partial Correlation r(xz.y)0.988682211673432
Correlation r(yz)0.89643119900075
Partial Correlation r(yz.x)-0.151721858663955



Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
(rho12 <- cor(x, y))
(rho23 <- cor(y, z))
(rho13 <- cor(x, z))
(rhoxy_z <- (rho12-(rho13*rho23))/(sqrt(1-(rho13*rho13)) * sqrt(1-(rho23*rho23))))
(rhoxz_y <- (rho13-(rho12*rho23))/(sqrt(1-(rho12*rho12)) * sqrt(1-(rho23*rho23))))
(rhoyz_x <- (rho23-(rho12*rho13))/(sqrt(1-(rho12*rho12)) * sqrt(1-(rho13*rho13))))
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Pearson Product Moment Partial Correlation - Ungrouped Data',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistic',1,TRUE)
a<-table.element(a,'Value',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation r(xy)',header=TRUE)
a<-table.element(a,rho12)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('partial_correlation1.htm','Partial Correlation r(xy.z)',''),header=TRUE)
a<-table.element(a,rhoxy_z)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation r(xz)',header=TRUE)
a<-table.element(a,rho13)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('partial_correlation1.htm','Partial Correlation r(xz.y)',''),header=TRUE)
a<-table.element(a,rhoxz_y)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation r(yz)',header=TRUE)
a<-table.element(a,rho23)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('partial_correlation1.htm','Partial Correlation r(yz.x)',''),header=TRUE)
a<-table.element(a,rhoyz_x)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')