alexa Size and Power Properties of Some Test Statistics for Testing the Population Correlation Coefficient ρ
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Size and Power Properties of Some Test Statistics for Testing the Population Correlation Coefficient ρ

Shipra Banik1 and Golam Kibria BM2*

1Department of Physical Sciences Independent University, Bangladesh Dhaka 1229, Bangladesh

2Department of Mathematics and Statistics Florida, International University Miami, FL 33199, USA

*Corresponding Author:
Golam Kibria BM
Department of Mathematics and Statistics Florida
International University Miami, FL 33199, USA
Tel: +1 305-348-2000
E-mail: [email protected]

Received date: May 31, 2017; Accepted date: June 16, 2017; Published date: June 30, 2017

Citation: Banik S, Golam Kibria BM (2017) Size and Power Properties of Some Test Statistics for Testing the Population Correlation Coefficient ρ. J Biom Biostat 8: 353. doi: 10.4172/2155-6180.1000353

Copyright: © 2017 Banik S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Correlation measures the strength of association between two variables, which plays an important role in various fields, such as Health Science, Economics, Finance, Engineering, Environmental science among others. Several tests for testing the population correlation coefficient are proposed in a literature by various researchers at different time points. This paper evaluates the performance of some of the prominent test statistics for testing the population correlation coefficient based on empirical size and power of the tests. Some bivariate distributions, such as normal, lognormal, gamma and chi-square are considered to compare the performance of the test statistics. We believe that the findings of this paper will make an important contribution to select some good test statistics to find the relationship between two variables.


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