An Introduction to Mathematical Statistics

Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In An Introduction to Mathematical Statistics the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model. Each chapter ends with a case study in which the described statistical methods are applied. This book assumes a basic knowledge of probability theory, calculus, and linear algebra. Several annexes are available for Mathematical Statistics on this page. Statistics is the science that focuses on drawing conclusions from data. To this end, collected data is modeled and analyzed using mathematical probability models. In Introduction to Statistics, key concepts from statistics are described and a mathematical basis for important statistical methods is provided. In addition, much attention is paid to the responsible application of statistical methods to data. The three most important subjects from statistics are estimators, tests, and reliability areas. Furthermore, well-known regression models, such as linear regression and analysis of variance, are discussed. Optimality of estimators and tests is also discussed in this book, as well as the selection of the most suitable model. Each chapter ends with a practical example in which described statistical methods are applied. This book is based on basic knowledge of probability theory, calculus, and linear algebra for the reader The field of statistics focuses on drawing conclusions from data by modeling and analyzing the data using probabilistic models. The authors of this introductory text describe three key concepts from statistics-estimators, tests, and confidence regions-which they demonstrate and apply in an extensive variety of examples and case studies. An entire chapter covers regression models, including linear regression and analysis of variance. This book, designed for students, assumes a basic knowledge of probability theory, calculus, and linear algebra

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