Matrix Analysis Statistics by Schott James (24 results)

Hardcover. Condition: Fair. No Jacket. 2nd Edition. Pages and covers in good shape, but front cover separated and repaired with tape.

Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Hardcover. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condition: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition.Please note:-May include library or rental stickers, stamps, or markings.-Supplemental materials e.g., CDs, access codes, inserts are not guaranteed.-Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. -Sourced from donation centers; authenticity not verified with publisher. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading!

Condition: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,850grams, ISBN:9780471669838.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: new.

Condition: New.

Condition: New. pp. 544.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

hardcover. Condition: new.

Condition: New. An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Series: Wiley Series in Probability and Statistics. Num Pages: 552 pages. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 242 x 158 x 33. Weight in Grams: 870. . 2016. 3rd Edition. Hardcover. . . . .

Condition: New. pp. 544.

Condition: New.

Hardcover. Condition: Very Good. Very Good. Dust Jacket may NOT BE INCLUDED.CDs may be missing. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New. An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Series: Wiley Series in Probability and Statistics. Num Pages: 552 pages. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 242 x 158 x 33. Weight in Grams: 870. . 2016. 3rd Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Condition: Brand New. 3rd edition. 520 pages. 10.00x6.50x1.25 inches. In Stock.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: As New. Unread book in perfect condition.

Buch. Condition: Neu. Neuware - An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practiceProviding accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms.An ideal introduction to matrix analysis theory and practice, Matrix Analysis for Statistics, Third Edition features:\* New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors\* Additional problems and chapter-end practice exercises at the end of each chapter\* Extensive examples that are familiar and easy to understand\* Self-contained chapters for flexibility in topic choice\* Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matricesMatrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics.James R. Schott, PhD, is Professor in the Department of Statistics at the University of Central Florida. He has published numerous journal articles in the area of multivariate analysis. Dr. Schott's research interests include multivariate analysis, analysis of covariance and correlation matrices, and dimensionality reduction techniques.

Hardcover. Condition: new. Hardcover. An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms. An ideal introduction to matrix analysis theory and practice, Matrix Analysis for Statistics, Third Edition features: New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors Additional problems and chapter-end practice exercises at the end of each chapter Extensive examples that are familiar and easy to understand Self-contained chapters for flexibility in topic choice Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matrices Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics. James R. Schott, PhD, is Professor in the Department of Statistics at the University of Central Florida. He has published numerous journal articles in the area of multivariate analysis. Dr. Schotts research interests include multivariate analysis, analysis of covariance and correlation matrices, and dimensionality reduction techniques. An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: Brand New. 3rd edition. 520 pages. 10.00x6.50x1.25 inches. In Stock. This item is printed on demand.

Hardcover. Condition: new. Hardcover. An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms. An ideal introduction to matrix analysis theory and practice, Matrix Analysis for Statistics, Third Edition features: New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors Additional problems and chapter-end practice exercises at the end of each chapter Extensive examples that are familiar and easy to understand Self-contained chapters for flexibility in topic choice Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matrices Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics. James R. Schott, PhD, is Professor in the Department of Statistics at the University of Central Florida. He has published numerous journal articles in the area of multivariate analysis. Dr. Schotts research interests include multivariate analysis, analysis of covariance and correlation matrices, and dimensionality reduction techniques. An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.