Textbook for Fundamental Courses in Science and Engineering at Higher Education Institutions - A Course in Probability Theory and Mathematical Statistics (Second Edition)

Author: Yang Yongfa
Publisher:
Publish Date: 2005-11-01
Features: This textbook is designed for the "Probability Theory and Mathematical Statistics" course in engineering universities. The book is divided into two parts: Probability Theory and Mathematical Statistics. The first four chapters cover Probability Theory, including fundamental concepts of probability theory, random variables and their probability distributions, the numerical characteristics of random variables, and the law of large numbers and the central limit theorem. The last five chapters cover Mathematical Statistics, including fundamental concepts of mathematical statistics, parameter estimation, hypothesis testing, analysis of variance, and regression analysis. At the end of the book, there is an introduction to the application of the mathematical software MATLAB in probability and statistics, a glossary, a table of common probability distributions, exercises, and reference answers to exercises. This book is closely aligned with the practical aspects of engineering teaching, emphasizing the integration of theory and practice. The selection of material is appropriate, the arguments are rigorous, and the organization is clear, making it convenient for teaching and self-study by students. It can serve as a textbook for undergraduate and graduate courses in "Probability Theory and Mathematical Statistics" in higher engineering colleges and is also suitable for non-mathematics majors in science and management disciplines.
Introduction: This textbook is designed for the "Probability Theory and Mathematical Statistics" course in engineering universities. The book is divided into two parts: Probability Theory and Mathematical Statistics. The first four chapters cover Probability Theory, including fundamental concepts of probability theory, random variables and their probability distributions, the numerical characteristics of random variables, and the law of large numbers and the central limit theorem. The last five chapters cover Mathematical Statistics, including fundamental concepts of mathematical statistics, parameter estimation, hypothesis testing, analysis of variance, and regression analysis. At the end of the book, there is an introduction to the application of the mathematical software MATLAB in probability and statistics, a glossary, a table of common probability distributions, exercises, and reference answers to exercises. This book is closely aligned with the practical aspects of engineering teaching, emphasizing the integration of theory and practice. The selection of material is appropriate, the arguments are rigorous, and the organization is clear, making it convenient for teaching and self-study by students. It can serve as a textbook for undergraduate and graduate courses in "Probability Theory and Mathematical Statistics" in higher engineering colleges and is also suitable for non-mathematics majors in science and management disciplines.