Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere." ―IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: <ul>
<li>Confirmatory Factor Analysis</li> <li>Classification Trees</li> <li>Dynamic Graphics</li> <li>Transformations to Normality</li> <li>Prediction for Multivariate Multiple Regression</li> <li>Kronecker Products and Vec Notation</li> </ul>
New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.

Methods of Multivariate Analysis

Science & Math

Rencher, Alvin C.

Christensen, William F.

Mathematics

wiley

This set contains: 9780471221746 SAS for Linear Models, Fourth Edition by Ramon Littell, Walter W. Stroup, Rudolf Freund and 9780471754985 Linear Models in Statistics, Second Edition by Alvin C. Rencher, G. Bruce Shaalje.

SAS System for Linear Models, 4e + Linear Models in Statistics, 2e Set

Schaalje, G. Bruce

Stroup, Walter W.

Freund, Rudolf

Littell, Ramon

Wiley-Interscience

Linear models made easy with this unique introduction Linear Models in Statistics discusses classical linear models from a matrix algebra perspective, making the subject easily accessible to readers encountering linear models for the first time. It provides a solid foundation from which to explore the literature and interpret correctly the output of computer packages, and brings together a number of approaches to regression and analysis of variance that more experienced practitioners will also benefit from. With an emphasis on broad coverage of essential topics, Linear Models in Statistics carefully develops the basic theory of regression and analysis of variance, illustrating it with examples from a wide range of disciplines. Other features of this remarkable work include: * Easy-to-read proofs and clear explanations of concepts and procedures * Special topics such as multiple regression with random x's and the effect of each variable on R¯2 * Advanced topics such as mixed and generalized linear models as well as logistic and nonlinear regression * The use of real data sets in examples, with all data sets available over the Internet * Numerous theoretical and applied problems, with answers in an appendix * A thorough review of the requisite matrix algebra * Graphs, charts, and tables as well as extensive references

Linear Models in Statistics

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Methods of Multivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. Multivariate analysis enables researchers to explore the joint performance of such variables and to determine the effect of each variable in the presence of the others. The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline. To illustrate multivariate applications, the author provides examples and exercises based on fifty-nine real data sets from a wide variety of scientific fields. Rencher takes a "methods" approach to his subject, with an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. The Second Edition contains revised and updated chapters from the critically acclaimed First Edition as well as brand-new chapters on: <ul>
<li> Cluster analysis </li> <li> Multidimensional scaling </li> <li> Correspondence analysis </li> <li> Biplots </li> </ul> Each chapter contains exercises, with corresponding answers and hints in the appendix, providing students the opportunity to test and extend their understanding of the subject. Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians.

Methods of Multivariate Analysis (Wiley Series in Probability and Statistics)

The most accessible introduction to the theory and practice of multivariate analysis Multivariate Statistical Inference and Applications is a user-friendly introduction to basic multivariate analysis theory and practice for statistics majors as well as nonmajors with little or no background in theoretical statistics. Among the many special features of this extremely accessible first text on multivariate analysis are: * Clear, step-by-step explanations of all key concepts and procedures along with original, easy-to-follow proofs * Numerous problems, examples, and tables of distributions * Many real-world data sets drawn from a wide range of disciplines * Reviews of univariate procedures that give rise to multivariate techniques * An extensive survey of the world literature on multivariate analysis * An in-depth review of matrix theory * A disk including all the data sets and SAS command files for all examples and numerical problems found in the book These same features also make Multivariate Statistical Inference and Applications an excellent professional resource for scientists and clinicians who need to acquaint themselves with multivariate techniques. It can be used as a stand-alone introduction or in concert with its more methods-oriented sibling volume, the critically acclaimed Methods of Multivariate Analysis.

Multivariate Statistical Inference and Applications

Researchers involved in the collection of scientific data often end up with multivariate systems. When several variables are simultaneously measured on the same experimental unity, they are usually correlated, and the pattern formed is often too difficult for the human mind to grasp. This text discusses in detail many proven techniques for finding the dimensionality of the pattern and unravelling the information contained in the complexity of variables. The book includes exercises and solutions as well as 41 data sets taken from various areas of application, such as engineering, manufacturing, medicine, social science and economics.

The essential introduction to the theory and application of linear models―now in a valuable new edition 
Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: <ul>
<li> New chapters on Bayesian linear models as well as random and mixed linear models </li> <li> Expanded discussion of two-way models with empty cells </li> <li> Additional sections on the geometry of least squares </li> <li> Updated coverage of simultaneous inference </li> </ul>
The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

See the best price to sell, buy, or rent books by Rencher, Alvin C.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Rencher, Alvin C.

See the best price to sell, buy, or rent books by Rencher, Alvin C.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Methods of Multivariate Analysis

SAS System for Linear Models, 4e + Linear Models in Statistics, 2e Set

Linear Models in Statistics

Methods of Multivariate Analysis (Wiley Series in Probability and Statistics)

Multivariate Statistical Inference and Applications

Methods of Multivariate Analysis

Linear Models in Statistics

Methods of Multivariate Analysis (Wiley Series in Probability and Statistics)

Methods of Multivariate Analysis (Wiley Series in Probability and Statistics)