Matrix Algebra: An Introduction (Quantitative Applications in the Social Sciences) [Paperback]

Krishnan Namboodiri (Author)

Book Description

Publication Date: January 1, 1984 | ISBN-10: 0803920520 | ISBN-13: 978-0803920521

Matrix Algebra is a vital tool for mathematics in the social sciences, and yet many social scientists have only a rudimentary grasp of it. This volume serves as a complete introduction to matrix algebra, requiring no background knowledge beyond basic school algebra. Namboodiri's presentation is smooth and readable: it begins with the basic definitions and goes on to explain elementary manipulations and the concept of linear dependence, eigenvalues, and eigenvectors -- supplying illustrations through fully-worked examples.

Product Details

• Paperback: 96 pages
• Publisher: Sage Publications, Inc (January 1, 1984)
• Language: English
• ISBN-10: 0803920520
• ISBN-13: 978-0803920521
• Product Dimensions: 8 x 6 x 0.2 inches
• Shipping Weight: 4 ounces (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #314,320 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

6 of 7 people found the following review helpful:

2.0 out of 5 stars Matrix Math the Hard Way, September 7, 2010

By 

John M. Ford "johnDC" (near DC, MD USA) - See all my reviews
(TOP 1000 REVIEWER)    (REAL NAME)   

This review is from: Matrix Algebra: An Introduction (Quantitative Applications in the Social Sciences) (Paperback)

Krishnan Namboodiri sets out to remedy the problem that "...social science majors and graduate students often fail to go far enough in mathematics to get a thorough grounding in [matrix algebra]." This little green book covers the ground in four chapters and 96 pages.

The author begins by introducing basic matrix concepts and terminology, moving quickly to matrix operations such as addition, multiplication, and inversion. The central concept of linear dependence of sets of matrix rows and matrix columns is then explored, including the implications of this concept for solving simultaneous linear equations. The final chapter introduces the concepts of determinants, eigenvectors and eigenvalues. Applications to principle components analysis are illustrated in the books closing sections.

I read this book as part of a three-week online review course in matrix algebra. Its strengths were the discussion of practical applications and its conciseness. These strengths are counterbalanced by disappointing weaknesses. The dense, formula-driven presentation style made the book hard to follow. A glossary and index would have greatly aided mastery of a large number of new terms and concepts. In the end, I was helped more in the class by free materials I found by searching the web than by this book. I was annoyed that I had purchased it.

After further searching, I became a grateful reader of Charles Cullen's Matrices and Linear Transformations, which is more clear, covers more material, includes numerous practice exercises--and requires less of a little green investment. Save yourself some pain and go directly to Cullen.

5.0 out of 5 stars excellent service, February 14, 2012

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jane - See all my reviews

This review is from: Matrix Algebra: An Introduction (Quantitative Applications in the Social Sciences) (Paperback)

The product was delivered on time and in perfect condition. I have purchased lots of text books over the years from amazon and have never been disappointed. Thanks!

3.0 out of 5 stars Somewhat Adequate, November 1, 2011

By 

Sirisha Sunkara - See all my reviews

Amazon Verified Purchase

This review is from: Matrix Algebra: An Introduction (Quantitative Applications in the Social Sciences) (Paperback)

This book seemed mostly to be a review, rather than an introduction to the concepts in Linear Algebra, which worked for me.

That said, some of the essential material such as concepts in 'generalized inverse' of matrices seemed to have been dealt with in a rush. The author could have been a bit more elaborate and lucid in his explanation of such concepts. Some details were clearly skipped over.
The chapter that talks about Eigenspace and Pricipal Component Analysis was really well written.

I got a cheaper reprint of this book, and the pages kept coming off, which made it really difficult to read.
In essence, this is a somewhat adequate book. However, there seem to be better books covering this kind of material in my opinion.