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Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
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Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
Matrix algebra is one of the most important areas of mathematics in data science and statistical theory, providing essential updates and comprehensive coverage on critical topics.
Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
商品＃: 210910921

Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)

商品＃: 210910921

TWD 4582

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Matrix algebra is one of the most important areas of mathematics in data science and statistical theory, providing essential updates and comprehensive coverage on critical topics.
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What Stands Out

Comprehensive Coverage
This book offers an extensive exploration of matrix algebra, making it an essential resource for students and professionals in statistics and data science. It covers fundamental concepts and advanced applications thoroughly.
Practical Applications
It connects theoretical concepts to real-world applications, helping readers understand how matrix algebra is used in statistical modeling and data analysis, thereby enhancing practical skills and knowledge.
User-Friendly Format
The Kindle edition provides easy navigation and access to hyperlinks, allowing for efficient studying and reference. Its well-structured content caters to both beginners and experienced researchers.

產品詳情

Shop Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics) online at a best price in Taiwan. B077NH917X
Publisher Springer
Publication date October 12, 2017
Edition 2nd
Language English
File size 13.4 MB
Enhanced typesetting Not Enabled
X-Ray for textbooks Enabled
Word Wise Not Enabled
Print length 677 pages
Format Print Replica
ISBN-13 978-3319648675
Page Flip Not Enabled
Part of series Springer Texts in Statistics
Item Weight1 lbs (450 grams)

Who Should Buy?

Suitable For
  • Statistics Students

    Ideal for students seeking to understand matrix algebra concepts used in statistical applications and computations.

  • Data Scientists

    Beneficial for data professionals needing matrix algebra for data analysis and statistical modeling in their work.

  • Researchers

    Helpful for researchers in statistics requiring deep theoretical knowledge and computational techniques for matrix algebra.

Not Suitable For
  • Beginners

    Not suitable for absolute beginners in algebra, as it assumes prior knowledge of matrix concepts.

產品描述

Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)

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客戶問答

  • 問題: Is this book suitable for beginners?

    Answer: This textbook is designed for graduate and advanced undergraduate students already familiar with basic concepts.
  • 問題: Do I need to know programming to use this textbook?

    Answer: Some understanding of programming in Fortran or C, or using R or Matlab is beneficial for the numerical linear algebra section.
  • 問題: What changes were made in the second edition?

    Answer: The second edition includes 100 pages of new material, 30 additional exercises, and new discussions on vectors and statistical applications.

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優點

  • Clear and comprehensive explanations
  • Well-structured for learning
  • Numerous practical examples
  • Strong focus on applications
  • Engaging writing style

缺點

  • Some sections are a bit dense.

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特色和優勢

  • Comprehensive coverage of matrix algebra for statistical applications.
  • Includes updates and additional material in the second edition.
  • Ideal for graduate and advanced undergraduate students.
  • Covers various types of matrices and their special properties.
  • Focus on numerical linear algebra and efficient algorithms.
  • Supplementary text for courses in linear models and statistical computing.

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