The book "Statistical Inference" by George Casella and Roger L. Berger is a comprehensive textbook that builds theoretical statistics from the fundamentals of probability theory. The second edition, published in 2024, covers all topics typically included in a standard inference course, such as distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. The authors emphasize practical applications and understanding basic statistical concepts, making it suitable for advanced undergraduate and graduate students in statistics. Key features of the book include: - A detailed development of statistical inference from probability theory. - Emphasis on practical uses of statistical theory, focusing on understanding and deriving reasonable statistical procedures. - Expansion of asymptotic methods and computational techniques, such as bootstrapping and the EM algorithm. - Reorganization of some chapters for clarity and improved structure. - New sections on pivoting, p-values, and robust regression. - A new chapter on large sample inference, covering topics like the delta method, consistency, and robust estimators. - Detailed examples and exercises to reinforce learning. The book is designed to be used in a one-year course, with flexible coverage options to suit different levels of mathematical background. It includes references for further study and is supported by a list of tables and figures. The authors, George Casella and Roger L. Berger, are renowned statisticians with extensive contributions to the field, and the book has been translated into multiple languages.The book "Statistical Inference" by George Casella and Roger L. Berger is a comprehensive textbook that builds theoretical statistics from the fundamentals of probability theory. The second edition, published in 2024, covers all topics typically included in a standard inference course, such as distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. The authors emphasize practical applications and understanding basic statistical concepts, making it suitable for advanced undergraduate and graduate students in statistics. Key features of the book include: - A detailed development of statistical inference from probability theory. - Emphasis on practical uses of statistical theory, focusing on understanding and deriving reasonable statistical procedures. - Expansion of asymptotic methods and computational techniques, such as bootstrapping and the EM algorithm. - Reorganization of some chapters for clarity and improved structure. - New sections on pivoting, p-values, and robust regression. - A new chapter on large sample inference, covering topics like the delta method, consistency, and robust estimators. - Detailed examples and exercises to reinforce learning. The book is designed to be used in a one-year course, with flexible coverage options to suit different levels of mathematical background. It includes references for further study and is supported by a list of tables and figures. The authors, George Casella and Roger L. Berger, are renowned statisticians with extensive contributions to the field, and the book has been translated into multiple languages.