**Statistical Inference, Second Edition** by George Casella and Roger L. Berger is a comprehensive textbook that introduces the theoretical foundations of statistics, building upon the principles of probability theory. The book is designed for graduate students in statistics and advanced undergraduate students with a strong mathematical background. It emphasizes practical applications of statistical theory, focusing on understanding fundamental statistical concepts and deriving reasonable statistical procedures rather than formal optimality considerations. The textbook covers all key topics in a standard statistical inference course, including distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. It features a lucid writing style, with hundreds of examples and exercises to aid understanding. Each chapter includes an extensive set of graduated exercises, allowing students to apply the concepts they have learned. The second edition, originally published by Cengage Learning, Inc. in 2001, has been updated and expanded to include more emphasis on asymptotic methods, computational techniques, and practical applications. The book has been restructured for clarity, with new sections on the EM algorithm, p-values, and large-sample inference. It also includes a new chapter on large-sample inference, covering the delta method, consistency, asymptotic normality, bootstrapping, and robust estimators. The book is organized into 12 chapters, starting with an introduction to probability theory and progressing through topics such as data reduction, point estimation, hypothesis testing, and interval estimation. It concludes with chapters on analysis of variance, regression, and special topics in statistical inference. The text is supported by a variety of tables, figures, and examples, making it an essential resource for students and professionals in the field of statistics.**Statistical Inference, Second Edition** by George Casella and Roger L. Berger is a comprehensive textbook that introduces the theoretical foundations of statistics, building upon the principles of probability theory. The book is designed for graduate students in statistics and advanced undergraduate students with a strong mathematical background. It emphasizes practical applications of statistical theory, focusing on understanding fundamental statistical concepts and deriving reasonable statistical procedures rather than formal optimality considerations. The textbook covers all key topics in a standard statistical inference course, including distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. It features a lucid writing style, with hundreds of examples and exercises to aid understanding. Each chapter includes an extensive set of graduated exercises, allowing students to apply the concepts they have learned. The second edition, originally published by Cengage Learning, Inc. in 2001, has been updated and expanded to include more emphasis on asymptotic methods, computational techniques, and practical applications. The book has been restructured for clarity, with new sections on the EM algorithm, p-values, and large-sample inference. It also includes a new chapter on large-sample inference, covering the delta method, consistency, asymptotic normality, bootstrapping, and robust estimators. The book is organized into 12 chapters, starting with an introduction to probability theory and progressing through topics such as data reduction, point estimation, hypothesis testing, and interval estimation. It concludes with chapters on analysis of variance, regression, and special topics in statistical inference. The text is supported by a variety of tables, figures, and examples, making it an essential resource for students and professionals in the field of statistics.