Matrix Algebra by Marco Taboga
Matrix Algebra by Marco Taboga - Table of Contents
1. The algebra of numeric arrays
2. Linear spaces
3. Matrix rank and inversion
4. Linear maps
5. Systems of linear equations
6. Special matrices and equivalence
7. Complex vectors and inner products
8. Determinants
9. Polynomials
10. Eigenvalues and eigenvectors
11. Matrix polynomials
12. Jordan form
13. Kronecker products and vectorizations
14. Matrix functions
15. Applications
What You Will Learn in Matrix Algebra by Marco Taboga
"Matrix Algebra" by Marco Taboga is a beginner-friendly academic book that introduces matrix algebra with a strong emphasis on applications in statistics and econometrics. Written in a clear and logical style, the book is designed for undergraduate and early graduate students who may not have a strong mathematical background but need a solid understanding of matrices for data-related fields.
The book covers essential topics such as matrix operations, linear dependence, rank, determinants, eigenvalues, and eigenvectors. Special attention is given to "statistical matrices", including projection matrices and positive definite matrices, which are commonly used in linear regression and probability theory. Through step-by-step explanations, the author makes complex ideas accessible and practical.
One of the book’s key strengths is its focus on learning by doing. Each concept is supported by detailed examples and fully solved exercises, making it ideal for self-study. By combining theory with application, "Matrix Algebra" helps readers build confidence in "linear algebra", understand "matrix theory", and apply mathematical tools effectively in "statistics", "econometrics", and "data analysis".
Book Details & Specifications
Title:
Matrix Algebra by Marco Taboga
Publisher:
StatLect
Year:
2021
Pages:
320
Type:
PDF
Language:
English
ISBN-10 #:
B096J3C6GR
ISBN-13 #:
979-8514136292
License:
External Educational Resource
Amazon:
Amazon
About the Author: Marco Taboga
The author Marco Taboga
is an applied mathematician and economist known for his ability to explain complex mathematical ideas in a clear and practical way. He holds a PhD in applied mathematics and has a strong academic background in economics and finance, which shapes his teaching and writing style. He is the author of "Matrix Algebra", a widely used resource in "linear algebra", "statistics", "econometrics", "quantitative methods", and "data analysis". His work focuses on helping students and professionals apply mathematical concepts effectively in real-world economic and statistical problems.
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