Practical Multivariate Analysis: (Chapman & Hall/CRC Texts in Statistical Science)

This new version of the bestselling Computer-Aided Multivariate Analysis has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods, Practical Multivariate Analysis, Fifth Edition shows readers how to perform multivariate statistical analyses and understand the results. For each of the techniques presented in this edition, the authors use the most recent software versions available and discuss the most modern ways of performing the analysis. New to the Fifth Edition Chapter on regression of correlated outcomes resulting from clustered or longitudinal samples Reorganization of the chapter on data analysis preparation to reflect current software packages Use of R statistical software Updated and reorganized references and summary tables Additional end-of-chapter problems and data sets The first part of the book provides examples of studies requiring multivariate analysis techniques; discusses characterizing data for analysis, computer programs, data entry, data management, data clean-up, missing values, and transformations; and presents a rough guide to assist in choosing the appropriate multivariate analysis. The second part examines outliers and diagnostics in simple linear regression and looks at how multiple linear regression is employed in practice and as a foundation for understanding a variety of concepts. The final part deals with the core of multivariate analysis, covering canonical correlation, discriminant, logistic regression, survival, principal components, factor, cluster, and log-linear analyses. While the text focuses on the use of R, S-PLUS, SAS, SPSS, Stata, and STATISTICA, other software packages can also be used since the output of most standard statistical programs is explained. Data sets and code are available for download from the book’s web page and CRC Press Online.

Table of Contents:
PREPARATION FOR ANALYSIS What Is Multivariate Analysis? Defining multivariate analysis Examples of multivariate analyses Multivariate analyses discussed in this book Organization and content of the book Characterizing Data for Analysis Variables: their definition, classification, and use Defining statistical variables Stevens’s classification of variables How variables are used in data analysis Examples of classifying variables Other characteristics of data Preparing for Data Analysis Processing data so they can be analyzed Choice of a statistical package Techniques for data entry Organizing the data Example: depression study Data Screening and Transformations Transformations, assessing normality and independence Common transformations Selecting appropriate transformations Assessing independence Selecting Appropriate Analyses Which analyses to perform? Why selection is often difficult Appropriate statistical measures Selecting appropriate multivariate analyses APPLIED REGRESSSION ANALYSIS Simple Regression and Correlation Chapter outline When are regression and correlation used? Data example Regression methods: fixed-X case Regression and correlation: variable-X case Interpretation: fixed-X case Interpretation: variable-X case Other available computer output Robustness and transformations for regression Other types of regression Special applications of regression Discussion of computer programs What to watch out for Multiple Regression and Correlation Chapter outline When are regression and correlation used? Data example Regression methods: fixed-X case Regression and correlation: variable-X case Interpretation: fixed-X case Interpretation: variable-X case Regression diagnostics and transformations Other options in computer programs Discussion of computer programs What to watch out for Variable Selection in Regression Chapter outline When are variable selection methods used? Data example Criteria for variable selection A general F test Stepwise regression Subset regression Discussion of computer programs Discussion of strategies What to watch out for Special Regression Topics Chapter outline Missing values in regression analysis Dummy variables Constraints on parameters Regression analysis with multicollinearity Ridge regression MULTIVARIATE ANALYSIS Canonical Correlation Analysis Chapter outline When is canonical correlation analysis used? Data example Basic concepts of canonical correlation Other topics in canonical correlation Discussion of computer program What to watch out for Discriminant Analysis Chapter outline When is discriminant analysis used? Data example Basic concepts of classification Theoretical background Interpretation Adjusting the dividing point How good is the discrimination? Testing variable contributions Variable selection Discussion of computer programs What to watch out for Logistic Regression Chapter outline When is logistic regression used? Data example Basic concepts of logistic regression Interpretation: Categorical variables Interpretation: Continuous variables Interpretation: Interactions Refining and evaluating logistic regression Nominal and ordinal logistic regression Applications of logistic regression Poisson regression Discussion of computer programs What to watch out for Regression Analysis with Survival Data Chapter outline When is survival analysis used? Data examples Survival functions Common survival distributions Comparing survival among groups The log-linear regression model The Cox regression model Comparing regression models Discussion of computer programs What to watch out for Principal Components Analysis Chapter outline When is principal components analysis used? Data example Basic concepts Interpretation Other uses Discussion of computer programs What to watch out for Factor Analysis Chapter outline When is factor analysis used? Data example Basic concepts Initial extraction: principal components Initial extraction: iterated components Factor rotations Assigning factor scores Application of factor analysis Discussion of computer programs What to watch out for Cluster Analysis Chapter outline When is cluster analysis used? Data example Basic concepts: initial analysis Analytical clustering techniques Cluster analysis for financial data set Discussion of computer programs What to watch out for Log-Linear Analysis Chapter outline When is log-linear analysis used? Data example Notation and sample considerations Tests and models for two-way tables Example of a two-way table Models for multiway tables Exploratory model building Assessing specific models Sample size issues The logit model Discussion of computer programs What to watch out for Correlated Outcomes Regression Chapter outline When is correlated outcomes regression used? Data example Basic concepts Regression of clustered data Regression of longitudinal data Other analyses of correlated outcomes Discussion of computer programs What to watch out for Appendix References Index A Summary and Problems appear at the end of each chapter.

Review :
Praise for Previous Editions: For the past 20 years, whenever I had an occasion to review a multivariate method!this was the book that I grabbed first. These books kept the mathematical content to the minimally necessary material and used a wealth of nice examples. One of its attractions is that it is a practical text that works well with nonstatisticians who have had a decent statistics course. It also continues to be an excellent book for the statistician's bookshelf. --Technometrics, November 2004 This book is an excellent presentation of computer-aided multivariate analysis. I believe that it will be a very useful addition to any scholarly library ! it provides a comprehensive introduction to available techniques for analyzing data of this form, written in a style that should appeal to non-specialists as well as to statisticians. --Zentralblatt MATH 105 This is a text for a broad spectrum of researchers ! who may find it very useful as it stresses the importance of understanding the concepts and methods through useful real life illustrations. --Journal of the RSS, Vol. 168, 2005 A key feature of this book is that it can be used in conjunction with any or all of the following very well-known software tools: S-Plus, SAS, SPSS, STATA, and STATISTICA. --Pat Altham, University of Cambridge, UK, Statistics in Medicine, 2005