| View Larger Image | Mathematical Tools for Applied Multivariate Analysis | Paperbackby J. Douglas Carroll (Author), Paul E. Green (Author), Anil Chaturvedi (Contributor)
| List Price: | $71.95 | | Price: | $64.75 | | You Save: | $7.20 (10%) | | | Available: | Usually ships in 24 hours |
| | Binding: | Paperback | | Publisher: | Academic Press | | Edition: | Rev Subth Edition | | Page Count: | 376 Pages | | Publication Date: | October 14, 1997 | | Sales Rank: | 853,680rd |
|
EDITORIAL REVIEWS |
Product Description
This revised edition presents the relevant aspects of transformational geometry, matrix algebra, and calculus to those who may be lacking the necessary mathematical foundations of applied multivariate analysis. It brings up-to-date many definitions of mathematical concepts and their operations. It also clearly defines the relevance of the exercises to concerns within the business community and the social and behavioral sciences. Readers gain a technical background for tackling applications-oriented multivariate texts and receive a geometric perspective for understanding multivariate methods."Mathematical Tools for Applied Multivariate Analysis, Revised Edition illustrates major concepts in matrix algebra, linear structures, and eigenstructures geometrically, numerically, and algebraically. The authors emphasize the applications of these techniques by discussing potential solutions to problems outlined early in the book. They also present small numerical examples of the various concepts. Key Features* Provides a technical base for tackling most applications-oriented multivariate texts* Presents a geometric perspective for aiding ones intuitive grasp of multivariate methods* Emphasizes technical terms current in the social and behavioral sciences, statistics, and mathematics* Can be used either as a stand-alone text or a supplement to a multivariate statistics textbook* Employs many pictures and diagrams to convey an intuitive perception of matrix algebra concepts* Toy problems provide a step-by-step approach to each model and matrix algebra concept* Provides solutions for all exercises |
CUSTOMER REVIEWS (Average Customer Rating: 5.0 based on 4 reviews)
|
Handy companion for multivariate statistics by Sunny (Chapel Hill, NC)  5 Stars
December 26, 2008
Understanding statistics, especially multivariate analysis, requires a command of linear algebra. Good texts are available presenting linear algebra for statistics (such as Harville). This text is a perfect supplement for learning linear algebra. It presents the geometric aspect of concepts in a manner that cannot be found in any other single text. If you really want to understand how eigenvectors represent a rotation of the regular basis, this book is for you. If you invest the time, this book provides you the tools to understand vector geometry for multivariate analysis. In addition, the text includes answers to exercises so it is great for self-study. I highly recommend this book.
| |
The mathematics of statistics by Michael Guldbrandtsen (Denmark) 5 Stars
October 10, 2008
A thorough treatment of the mathematical foundations of multivariate analysis. The book is full of graphical representations and a whole chapter is dedicated to a geometric interpretation of matrix operations. It starts out very softly with some basic matrix operations and from this it gradually extends the perspective to cover more complicated topics. I can highly recommend this book to anyone desiring a deeper understanding of what goes on behind the scenes of statical modelling.
| |
Excellent book by Peter Lind (HUDDINGE Sweden) 5 Stars
November 04, 2003
If you are in a situation like me, then this might be the best book. I read some introductory long ago at university. A few years from now I needed to refresh on the subject and get a little deeper. I have browsed or read some other books, but this is now my number one choice for reference. It starts very gently with e.g. spelled out examples on matrix addition. It ends with (by my standards) advanced topics like eigenstructures, quadratic forms, generalized inverses etc. I admire the writing style which is compact, precise and at the same time a little relaxed.
| |
A great introduction to mathematics of statistical analysis 5 Stars
December 06, 2001
There are a lot of people out there who do statistical analysis but who do not possess the mathematical knowledge underpinning a lot of what they are doing (i.e., linear algebra and some calculus). Most of the time people can get away with using stastical software as a sort of 'black box' and not worry about the math. But there are situations when having the background knowledge is crucial.This book does an excellent job of facilitating self-study of the math underpinning multivariate statistical analysis ... namely, linear (matrix) algebra and some calculus. Each chapter has a set of questions and ALL of the answers are provided in the book (handy for self-study). The one slight critique of this book I can give is that I wish the book did more on the calculus aspects. However, that is a minor comment and the knowledge that this book imparts of linear algebra to self-learners is extremely valuable.
| |
SIMILAR PRODUCTS |
| Analyzing Multivariate Data (with CD-ROM) (Duxbury Applied Series)
by James Lattin (Author), Douglas Carroll (Author), Paul Green (Author)
Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Green, and Carroll have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not yet advanced statistics majors. Their text accomplishes this through a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through...
| 
| Data Analysis Using Regression and Multilevel/Hierarchical Models
by Andrew Gelman (Author), Jennifer Hill (Author)
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied...
| 
| Logistic Regression Models (Chapman & Hall/ Crc: Texts in Statistical Science Series)
by Joseph M. Hilbe (Author)
Provides Comprehensive Coverage of All Types of Logistic Models
Ideal for Both Classroom Instruction and a Reference for Practicing Researchers and Statisticians
Based on a successful course taught by the author
Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized,...
| | | SPSS Companion for Lattin/Green/Carroll's Analyzing Multivariate Data
by James Lattin (Author), Paul Green (Author), Douglas Carroll (Author)
The annotated program output provided facilitates interpretation and links to the concepts included in the text.
| 
| Multivariate Statistical Analysis: A Conceptual Introduction
by Sam Kash Kachigan (Author)
This classic multivariate statistics book has become the introduction of choice for researchers and students with a minimal mathematics background. In addition to providing a review of fundamental statistical methods, it provides a basic treatment of advanced computer-based multivariate analytical techniques; including correlation and regression analysis, analysis of variance, discriminant analysis, factor analysis, cluster analysis, and multidimensional scaling. The conceptual treatment...
|
|
|
