Foundations and Applications of Statistical Theory

Statistical theory forms the foundation for analyzing, modeling, and interpreting data, enabling insights into complex systems and phenomena. It is grounded in mathematical principles and probability theory, providing the tools to quantify uncertainty, assess relationships, and make informed decisions. Central to statistical theory are parameter estimation, hypothesis testing, and model development, which offer structured methods for data-driven reasoning. The applications of statistical theory are diverse, spanning science, engineering, medicine, economics, social sciences, and technology. It supports tasks such as designing experiments, predicting outcomes, and optimizing processes in dynamic and uncertain environments. Statistical methodologies, including regression analysis, Bayesian inference, and non-parametric methods, adapt to the needs of different fields, ensuring flexibility and robustness in data analysis.

Autorentext

Adel Settati is a distinguished full professor of mathematics at the Faculty of Sciences and Technology (FSTT) in Tangier, Morocco. He earned his Ph.D. in probability from the prestigious University of Renne 1 in France.