It is with great pleasure and pride that we present this collaborative volume on Numerical Methods and Statistical Techniques, designed to serve as an essential reference for students, educators, and practitioners across disciplines such as engineering, computer science, mathematics, and data analytics. This book represents the collective efforts of multiple authors, each bringing their expertise and insights to develop a comprehensive, coherent, and accessible resource on these foundational topics. Numerical methods and statistical techniques form the backbone of modern scientific inquiry and engineering problem-solving. With the rapid advancement of computational technology and the exponential growth of data, there is an ever-increasing demand for reliable, efficient, and robust numerical algorithms and statistical methodologies. Addressing this need, our book integrates fundamental theories with practical algorithms and applications, offering readers a balanced understanding of concepts and hands-on approaches. The text is structured to cover a broad spectrum of subjects extensively. Beginning with the representation and manipulation of numbers in computing, the book progresses through classical numerical solutions for algebraic equations, interpolation methods, numerical differentiation and integration, and the numerical solution of differential equations—including Euler’s method, Taylor series method, and Runge-Kutta methods. Parallelly, the statistical segment introduces the history, fundamental concepts, and computation methods, progressing into probability distributions, estimation theory, hypothesis testing, regression, and inferential statistics. Special attention is given to clarity of presentation and pedagogical effectiveness. Each unit is thoughtfully arranged to facilitate understanding, featuring illustrative examples, exercises, and evaluation questions that encourage active learning. Moreover, the multi-author composition ensures expertise-driven coverage of each topic, enabling depth as well as breadth. We hope that this book equips readers with the analytical tools and computational skills necessary to tackle both theoretical and applied problems confidently. It aims to be not just a textbook but a lasting resource for reference and skill development in the dynamic and essential fields of numerical analysis and statistics. We sincerely thank all the contributors and institutions who supported this endeavor and look forward to the book serving as a valuable guide for learners and professionals alike.