Multivariate Approach to Partial Correlation Analysis

Onyeneke Casmir Chidiebere

doi:doi:10.11648/j.sjams.20150303.20

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Multivariate Approach to Partial Correlation Analysis

Onyeneke Casmir Chidiebere

Published in Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 3)

Received: 13 May 2015 Accepted: 29 May 2015 Published: 11 June 2015

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Abstract

Multivariate approach to generate variance covariance and partial correlation coefficients of one or more independent variables has been the concern of advanced statisticians and users of statistical tools. This work tackled the problem by keeping one or some variables constant and partitioned the variance covariance matrices to find multivariate partial correlations. Due to the challenges that faced the analysis and computation of complex variables, this research used matrix to ascertain the level of relationship that exist among these variables and obtained correlation coefficients from variance covariance matrices. It was proved that partial correlation coefficients are diagonal matrices that are normally distributed. (Work count = 101).

Published in	Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 3)
DOI	10.11648/j.sjams.20150303.20
Page(s)	165-170
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Multivariate, Correlation, Partial, Normality, Coefficients, Variables, Matrices

References

[1]	Anderson, T. W., (2003); An Introduction to Multivariate Statistical Analysis 3rd Edition. Wiley-Interscience, New York
[2]	Francois Husson, Sebastien Le and Jerome Pages (2010); Exploratory Multivariate Analysis. Chapman & Hall Books, retrieved from CRC Press www.crcpress.com
[3]	Kendall, M. G., and Stuart, A. (1973). The Advanced Theory of Statistics (Inference and Relationship). Vol. 2 (Third ed.). Hafner Publishing Co., New York.
[4]	Morison D. F., (2007); Multivariate statistical Methods. McGraw-Hill Book Company, New York. Retrieved from onlinelibrary.wiley.com.
[5]	Neter, John, Michael H. Kutner, Christopher Nachtsheim, and William Wasserman (1996). Applied Linear Statistical Models. McGraw Hill, Boston.
[6]	Partial Correlation Analysis retrieved from https://explorable.com/partial-correlation-analysis
[7]	Rao, C. R., (2008); Linear Statistical Inference and its Application: Second Edition. John Wiley & Sons Inc. Retrieved from onlinelibrary.wiley.com.
[8]	Regression Tutorial Menu Dictionary, STATS @ MTSU retrieved from http://mtweb.mtsu.edu/stats/regression/level3/multicorrel/multicorrcoef.htm
[9]	Steiger, J. H., and Browne, M. W. (1984). The Comparison of Interdependent Correlations between Optimal Linear composites. Psychometrika, 49, 11-24.
[10]	Stockwell Ian (2008); Introduction to Correlation and Regression. SAS Global Forum, Batimore.
[11]	Tabachnick, B., and Fidell L. (1989); Using Multivariate Statistics. Harper & Row Publishers, New York.

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Onyeneke Casmir Chidiebere. (2015). Multivariate Approach to Partial Correlation Analysis. Science Journal of Applied Mathematics and Statistics, 3(3), 165-170. https://doi.org/10.11648/j.sjams.20150303.20

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Onyeneke Casmir Chidiebere. Multivariate Approach to Partial Correlation Analysis. Sci. J. Appl. Math. Stat. 2015, 3(3), 165-170. doi: 10.11648/j.sjams.20150303.20

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Onyeneke Casmir Chidiebere. Multivariate Approach to Partial Correlation Analysis. Sci J Appl Math Stat. 2015;3(3):165-170. doi: 10.11648/j.sjams.20150303.20

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@article{10.11648/j.sjams.20150303.20,
  author = {Onyeneke Casmir Chidiebere},
  title = {Multivariate Approach to Partial Correlation Analysis},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {3},
  number = {3},
  pages = {165-170},
  doi = {10.11648/j.sjams.20150303.20},
  url = {https://doi.org/10.11648/j.sjams.20150303.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150303.20},
  abstract = {Multivariate approach to generate variance covariance and partial correlation coefficients of one or more independent variables has been the concern of advanced statisticians and users of statistical tools. This work tackled the problem by keeping one or some variables constant and partitioned the variance covariance matrices to find multivariate partial correlations. Due to the challenges that faced the analysis and computation of complex variables, this research used matrix to ascertain the level of relationship that exist among these variables and obtained correlation coefficients from variance covariance matrices. It was proved that partial correlation coefficients are diagonal matrices that are normally distributed. (Work count = 101).},
 year = {2015}
}

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TY  - JOUR
T1  - Multivariate Approach to Partial Correlation Analysis
AU  - Onyeneke Casmir Chidiebere
Y1  - 2015/06/11
PY  - 2015
N1  - https://doi.org/10.11648/j.sjams.20150303.20
DO  - 10.11648/j.sjams.20150303.20
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 165
EP  - 170
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20150303.20
AB  - Multivariate approach to generate variance covariance and partial correlation coefficients of one or more independent variables has been the concern of advanced statisticians and users of statistical tools. This work tackled the problem by keeping one or some variables constant and partitioned the variance covariance matrices to find multivariate partial correlations. Due to the challenges that faced the analysis and computation of complex variables, this research used matrix to ascertain the level of relationship that exist among these variables and obtained correlation coefficients from variance covariance matrices. It was proved that partial correlation coefficients are diagonal matrices that are normally distributed. (Work count = 101).
VL  - 3
IS  - 3
ER  -

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Author Information

Onyeneke Casmir Chidiebere

Mathematics and Statistics Department, University of Calabar, Calabar, Nigeria

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Onyeneke Casmir Chidiebere. (2015). Multivariate Approach to Partial Correlation Analysis. Science Journal of Applied Mathematics and Statistics, 3(3), 165-170. https://doi.org/10.11648/j.sjams.20150303.20

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ACS Style

Onyeneke Casmir Chidiebere. Multivariate Approach to Partial Correlation Analysis. Sci. J. Appl. Math. Stat. 2015, 3(3), 165-170. doi: 10.11648/j.sjams.20150303.20

Copy | Download

AMA Style

Onyeneke Casmir Chidiebere. Multivariate Approach to Partial Correlation Analysis. Sci J Appl Math Stat. 2015;3(3):165-170. doi: 10.11648/j.sjams.20150303.20

Copy | Download

@article{10.11648/j.sjams.20150303.20,
  author = {Onyeneke Casmir Chidiebere},
  title = {Multivariate Approach to Partial Correlation Analysis},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {3},
  number = {3},
  pages = {165-170},
  doi = {10.11648/j.sjams.20150303.20},
  url = {https://doi.org/10.11648/j.sjams.20150303.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150303.20},
  abstract = {Multivariate approach to generate variance covariance and partial correlation coefficients of one or more independent variables has been the concern of advanced statisticians and users of statistical tools. This work tackled the problem by keeping one or some variables constant and partitioned the variance covariance matrices to find multivariate partial correlations. Due to the challenges that faced the analysis and computation of complex variables, this research used matrix to ascertain the level of relationship that exist among these variables and obtained correlation coefficients from variance covariance matrices. It was proved that partial correlation coefficients are diagonal matrices that are normally distributed. (Work count = 101).},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Multivariate Approach to Partial Correlation Analysis
AU  - Onyeneke Casmir Chidiebere
Y1  - 2015/06/11
PY  - 2015
N1  - https://doi.org/10.11648/j.sjams.20150303.20
DO  - 10.11648/j.sjams.20150303.20
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 165
EP  - 170
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20150303.20
AB  - Multivariate approach to generate variance covariance and partial correlation coefficients of one or more independent variables has been the concern of advanced statisticians and users of statistical tools. This work tackled the problem by keeping one or some variables constant and partitioned the variance covariance matrices to find multivariate partial correlations. Due to the challenges that faced the analysis and computation of complex variables, this research used matrix to ascertain the level of relationship that exist among these variables and obtained correlation coefficients from variance covariance matrices. It was proved that partial correlation coefficients are diagonal matrices that are normally distributed. (Work count = 101).
VL  - 3
IS  - 3
ER  -

Copy | Download