This book explains the comparison of Statistical Technique and Machine Learning Technique, specifically the Regression Model and the Radial Basis Function Neural Network (RBFNN) Model. This explanation involves the Mathematical theory and principle upon which RBFNN model was developed and it manages the generally belief that Machine Learning techniques are "Black Box" meaning that the Mathematics of Neural Network cannot be explained. Therefore, this book explains the Mathematics of the Radial Basis Functions which depends on the Gaussian function. Some estimates of the two models were compared and explained in this book such as the Sum of Squares of Errors, Bayesian Information Criterion and the Relative Importance of each explanatory variable.