Applied Statistical Decision Theory by Howard Raiffa and Robert Schlaifer, published by Harvard University in 1961. The book is part of the Division of Research, Graduate School of Business Administration, Harvard University. It provides a comprehensive treatment of statistical decision theory, focusing on the application of statistical methods in decision-making processes. The authors present a systematic approach to decision-making under uncertainty, incorporating probability theory and statistical inference. The book is intended for graduate students and researchers in business administration and related fields. It covers topics such as decision trees, expected utility, risk analysis, and Bayesian inference. The authors emphasize the importance of statistical analysis in making informed decisions in business and management contexts. The book is structured to provide both theoretical foundations and practical applications of statistical decision theory. It includes numerous examples and case studies to illustrate the concepts discussed. The text is written in a clear and concise manner, making it accessible to readers with a background in statistics and business administration. The book is considered a foundational text in the field of statistical decision theory and has been widely used in academic settings. It serves as a valuable resource for students and professionals seeking to understand and apply statistical methods in decision-making processes. The publication is part of Harvard University's efforts to advance research and education in business administration.Applied Statistical Decision Theory by Howard Raiffa and Robert Schlaifer, published by Harvard University in 1961. The book is part of the Division of Research, Graduate School of Business Administration, Harvard University. It provides a comprehensive treatment of statistical decision theory, focusing on the application of statistical methods in decision-making processes. The authors present a systematic approach to decision-making under uncertainty, incorporating probability theory and statistical inference. The book is intended for graduate students and researchers in business administration and related fields. It covers topics such as decision trees, expected utility, risk analysis, and Bayesian inference. The authors emphasize the importance of statistical analysis in making informed decisions in business and management contexts. The book is structured to provide both theoretical foundations and practical applications of statistical decision theory. It includes numerous examples and case studies to illustrate the concepts discussed. The text is written in a clear and concise manner, making it accessible to readers with a background in statistics and business administration. The book is considered a foundational text in the field of statistical decision theory and has been widely used in academic settings. It serves as a valuable resource for students and professionals seeking to understand and apply statistical methods in decision-making processes. The publication is part of Harvard University's efforts to advance research and education in business administration.