Computer-Aided Multivariate Analysis, Fourth Edition: (Chapman & Hall/CRC Texts in Statistical Science)

Computer-Aided Multivariate Analysis, Fourth Edition enables researchers and students with limited mathematical backgrounds to understand the concepts underlying multivariate statistical analysis, perform analysis using statistical packages, and understand the output. New topics include Loess and Poisson regression, nominal and ordinal logistic regression, interpretation of interactions in logistic and survival analysis, and imputation for missing values. This book includes new exercises and references, and updated options in the latest versions of the statistical packages. All data sets and codebooks are available for download. The authors explain the assumptions made in performing each analysis and test, how to determine if your data meets those assumptions, and what to do if they do not. What to Watch out for sections in each chapter warn of common difficulties. By reading this text, you will know what method to use with your data set, how to get the results, and how to interpret them and explain them to others. New in the Fourth Edition: Expanded explanation of checking for goodness of fit in logistic regression and survival analysis Kaplan-Meier estimates of survival curves, formal tests for comparing survival between groups, interactions and the use of time-dependent covariates in survival analysis Expanded discussion of how to handle missing values Latest features of the S-PLUS package in addition to SAS, SPSS, STATA, and STATISTICA for multivariate analysis Data sets for the problems are available at the CRC web site: http://www.crcpress.com/product/isbn/9781584883081 Commands and output for examples used in the text for each statistical package are available at the UCLA web site: http://www.ats.ucla.edu/stat/examples/cama4/

Table of Contents:
Section 1: 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 ANALYSES 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 Section 2: Applied Regression 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 techniques Regression and correlation: variable-X case techniques 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 163 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 Section 3: 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 discriminant? 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 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 APPENDIX INDEX Each chapter also includes Summary, References, and Problems sections.

Review :
For the past 20 years, whenever I had and occasion to review a multivariate methodthis 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 libraryit 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