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Applied Matrix Algebra in the Statistical Sciences (Dover Books on Mathematics)

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Book Details:

  • Publisher: Dover

  • Author: Alexander Basilevsky

  • Language: English

  • Binding: Paperback

  • Edition: Illustrated Edition

  • ISBN: 9780486445380

  • Number of Pages: 389

  • Dimensions: 8.5 x 5.5 x 0.9 inches

  • Release Date: 27-12-2005

About The Book:

"Applied Matrix Algebra in the Statistical Sciences" by Alexander Basilevsky offers a comprehensive examination of both applied and theoretical aspects of matrix algebra within the context of statistical sciences. The book serves as a bridge between linear algebra and statistical models, making it an invaluable resource for advanced undergraduate and graduate students, as well as researchers seeking a practical understanding of matrix algebra in statistics.

The book is structured in two interrelated parts: the first focuses on the fundamentals of vectors and vector spaces, while the second delves into the properties of matrices and their linear transformations. Emphasizing the relationship between these mathematical structures and real-world statistical applications, the text provides readers with a clear understanding of how matrix algebra can be utilized in the field of statistics.

Each chapter concludes with exercises that illustrate real-world applications of matrix algebra, allowing students to solidify their knowledge through practical examples. The book’s approach is accessible, requiring only a basic understanding of high school mathematics and a first course in statistics. It also serves as a self-contained reference for anyone looking to deepen their understanding of matrix algebra and its role in statistical modeling.

Key Features:

  • Comprehensive Coverage: Explores both theoretical and applied matrix algebra, with a strong focus on real-world statistical applications.

  • Self-Contained: Suitable for advanced undergraduate and graduate students, with no advanced mathematical background required beyond high school math and an introductory statistics course.

  • Illustrative Exercises: Each chapter includes exercises that demonstrate how matrix algebra is applied in statistical contexts, helping students build a deeper understanding of the material.

  • Clear and Practical Approach: Focuses on the practical use of matrix algebra in the context of statistics, with numerous examples and applications.

Ideal for:

  • Students of Statistics and Mathematics: The book is well-suited for advanced undergraduate and graduate students studying statistics, linear algebra, or related fields.

  • Researchers and Practitioners: Useful as a reference for those applying matrix algebra in statistical modeling and analysis.

  • Self-learners: A great resource for individuals looking to study matrix algebra independently, particularly in the context of statistical science.

Conclusion:

"Applied Matrix Algebra in the Statistical Sciences" by Alexander Basilevsky is an essential resource for anyone studying or working with statistics and matrix algebra. With its clear, structured approach, this book provides both a theoretical foundation and practical applications, making it an ideal companion for students and professionals alike. The text’s emphasis on real-world examples and exercises enhances its value as both an instructional tool and a reference work in the field of applied statistics.