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ISSN: 2332-2071 Volume 8 Number 2A 2020 Special Edition on Synergy Through Diversity in Science and Mathematics Mathematics and Statistics http://www.hrpub.org Horizon Research Publishing, USA http://www.hrpub.org Mathematics and Statistics Mathematics and Statistics is an international peer-reviewed journal that publishes original and high-quality research papers in all areas of mathematics and statistics. As an important academic exchange platform, scientists and researchers can know the most up-to-date academic trends and seek valuable primary sources for reference. 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Mathematics and Statistics Editor-in-Chief Prof. Dshalalow Jewgeni Florida Inst. of Technology, USA Members of Editorial Board Jiafeng Lu Zhejiang Normal University, China Nadeem-ur Rehman Aligarh Muslim University, India Debaraj Sen Concordia University, Canada Mauro Spreafico University of São Paulo, Brazil Veli Shakhmurov Okan University, Turkey Antonio Maria Scarfone Institute of Complex Systems - National Research Council, Italy Liang-yun Zhang Nanjing Agricultural University, China Ilgar Jabbarov Ganja state university, Azerbaijan Mohammad Syed Pukhta Sher-e-Kashmir University of Agricultural Sciences and Technology, India Vadim Kryakvin Southern Federal University, Russia Rakhshanda Dzhabarzadeh National Academy of Science of Azerbaijan, Azerbaijan Sergey Sudoplatov Sobolev Institute of Mathematics, Russia Birol Altın Gazi University, Turkey Araz Aliev Baku State University, Azerbaijan Francisco Gallego Lupianez Universidad Complutense de Madrid, Spain Hui Zhang St. Jude Children's Research Hospital, USA Yusif Abilov Odlar Yurdu University, Azerbaijan Evgeny Maleko Magnitogorsk State Technical University, Russia İmdat İşcan Giresun University, Turkey Emanuele Galligani University of Modena and Reggio Emillia, Italy Mahammad Nurmammadov Baku State University, Azerbaijan Horizon Research Publishing http://www.hrpub.org ISSN: 2332-2071 Table of Contents Mathematics and Statistics Volume 8 Number 2A 2020 The Performance of Different Correlation Coefficient under Contaminated Bivariate Data (https://www.doi.org/10.13189/ms.2020.081301) Bahtiar Jamili Zaini, Shamshuritawati Sharif .................................................................................................................. 1 Approximate Analytical Solutions of Nonlinear Korteweg-de Vries Equations Using Multistep Modified Reduced Differential Transform Method (https://www.doi.org/10.13189/ms.2020.081302) Che Haziqah Che Hussin, Ahmad Izani Md Ismail, Adem Kilicman, Amirah Azmi ...................................................... 9 Bayesian Estimation in Piecewise Constant Model with Gamma Noise by Using Reversible Jump MCMC (https://www.doi.org/10.13189/ms.2020.081303) Suparman ....................................................................................................................................................................... 17 Weakly Special Classes of Modules (https://www.doi.org/10.13189/ms.2020.081304) Puguh Wahyu Prasetyo, Indah Emilia Wijayanti, Halina France-Jackson, Joe Repka .................................................. 23 Markov Chain: First Step towards Heat Wave Analysis in Malaysia (https://www.doi.org/10.13189/ms.2020.081305) Nur Hanim Mohd Salleh, Husna Hasan, Fariza Yunus .................................................................................................. 28 Robust Method in Multiple Linear Regression Model on Diabetes Patients (https://www.doi.org/10.13189/ms.2020.081306) Mohd Saifullah Rusiman, Siti Nasuha Md Nor, Suparman, Siti Noor Asyikin Mohd Razali ....................................... 36 An Alternative Approach for Finding Newton's Direction in Solving Large-Scale Unconstrained Optimization for Problems with an Arrowhead Hessian Matrix (https://www.doi.org/10.13189/ms.2020.081307) Khadizah Ghazali, Jumat Sulaiman, Yosza Dasril, Darmesah Gabda ........................................................................... 40 Parameter Estimations of the Generalized Extreme Value Distributions for Small Sample Size (https://www.doi.org/10.13189/ms.2020.081308) RaziraAniza Roslan, Chin Su Na, Darmesah Gabda ..................................................................................................... 47 Fourth-order Compact Iterative Scheme for the Two-dimensional Time Fractional Sub-diffusion Equations (https://www.doi.org/10.13189/ms.2020.081309) Muhammad Asim Khan, Norhashidah Hj. Mohd Ali .................................................................................................... 52 Hybrid Flow-Shop Scheduling (HFS) Problem Solving with Migrating Birds Optimization (MBO) Algorithm (https://www.doi.org/10.13189/ms.2020.081310) Yona Eka Pratiwi, Kusbudiono, Abduh Riski, Alfian Futuhul Hadi .............................................................................. 58 Mathematics and Statistics 8(2A): 1-8, 2020 http://www.hrpub.org DOI: 10.13189/ms.2020.081301 The Performance of Different Correlation Coefficient under Contaminated Bivariate Data Bahtiar Jamili Zaini*, Shamshuritawati Sharif School of Quantitative Sciences, Universiti Utara Malaysia, Malaysia Received July 31, 2019; Revised September 28, 2019; Accepted February 20, 2020 Copyright©2020 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract Bivariate data consists of 2 random variables these 2 variables. Besides scatter plot, correlation that are obtained from the same population. The coefficient can be used to measure the relationship between relationship between 2 bivariate data can be measured by 2 variables [1]. There are several types of correlation correlation coefficient. A correlation coefficient computed coefficients, such as Pearson correlation coefficient, from the sample data is used to measure the strength and Spearman rank correlation coefficient, Kendall’s Tau direction of a linear relationship between 2 variables. correlation coefficient, and other robust correlation However, the classical correlation coefficient results are methods. Correlation coefficient is a simple statistical inadequate in the presence of outliers. Therefore, this study measure of relationship between 2 random variables. The focuses on the performance of different correlation correlation coefficient computed from the sample data coefficient under contaminated bivariate data to determine measures the strength and direction of a linear relationship the strength of their relationships. We compared the between 2 variables [2]. If there is