Statistical modeling is a powerful technique used to analyze and interpret data, providing valuable insights into complex phenomena. At its core, it involves the use of mathematical equations and statistical methods to describe relationships between variables within a dataset. By identifying patterns and trends, statistical models enable researchers to make predictions and draw conclusions based on empirical evidence. There are various types of statistical models, each suited to different types of data and research questions. For example, linear regression models are commonly used to explore relationships between continuous variables, while logistic regression models are employed when the outcome variable is categorical. More advanced techniques, such as time series analysis, hierarchical modeling, and machine learning algorithms, offer even greater flexibility and predictive power. Statistical modeling plays a crucial role in fields ranging from economics and epidemiology to engineering and social sciences. It allows researchers to test hypotheses, validate theories, and make informed decisions based on data-driven insights. This book unravels the recent studies in the field of statistical modeling. Also included in this book is a detailed explanation of the various concepts and applications of statistical modeling. It is a collective contribution of a renowned group of international experts in this field.