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).
| DOI | 10.11648/j.sjams.20150303.20 |
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 3, June 2015) |
| 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), 2024. Published by Science Publishing Group |
Multivariate, Correlation, Partial, Normality, Coefficients, Variables, Matrices
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APA Style
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
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
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
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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}
}
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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