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Elements in a sample date are demonstrated based on their characteristics and in turn the characteristics are represented by variables. Identifying the relationship between these variables is crucial for prediction, hypothesis testing, and decision making. The relation between two variables is often quantified using a correlation factor. Once correlation is known it can be used to make predictions. It means when two variables are highly correlated, and if we have observed one variable, we can make a prediction about the other variable. A more accurate prediction will be made where there is strong relationship between variables. Among several correlation factors, Pearson correlation Coefficient has been commonly used. Distance correlation and maximal information coefficient have been introduced recently to address the shortcomings of Pearson correlation coefficient. In this paper, we compare these factors through a set of simulations and combine them to introduce a more robust factor that can be generally used.