Foundations and Applications of Statistics: A Computational Approach

This book presents a modern perspective on Statistics as both a scientific discipline and a powerful method for investigating real-world phenomena. Rooted in the need to understand variability, uncertainty, and complex processes, it integrates classical statistical theory with contemporary approaches based on information concepts such as entropy, informational energy, and correlation. Emphasizing practical relevance, the book focuses on measuring uncertainty, assessing risk, and supporting decision-making in situations where complete information is unattainable. It bridges theory and application by combining Probability Theory with statistical modeling techniques used in diverse domains. Designed for graduate students and practitioners, the text demonstrates how computational tools, particularly Matlab and its Statistical Toolbox, can simplify data analysis and enhance problem-solving efficiency. Through real-world examples drawn from finance, quality control, and other applied fields, readers gain insight into the versatility and power of statistical methods in addressing complex challenges.