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Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 347 million people are suffering from diabetes throughout the world. To overcome the sharp rise in the disease, various diagnostic or prediction models were developed through various techniques such as artificial intelligence, classification and clustering, pattern recognition and statistical methods. The study led to the related open issues of identifying the need of a relation between the major factors that lead to the development of diabetes. This is possible by investigating the links found between the independent and dependant variables in the dataset. This paper investigates the effect of binary logistic regression applied on a dataset. The results show that the most effective method was the enter method which gave a prediction accuracy of almost 93%.[Zehra A, Kadir TAA, Aznan MAM, Haq R, Badshah G. Statistical modeling for prediction of diabetes in Malaysians. Life Sci J 2018; 15 (6): 76-80]. ISSN: 1097-8135 (Print)/ISSN: 2372-613X (Online). http://www. lifesciencesite. com. 9. doi: 10.7537/marslsj150618. 09.