Practical Multivariate Analysis, Fifth Edition
By Abdelmonem Afifi, Susanne May, Virginia A. Clark
Published July 5th 2011 by Chapman and Hall/CRC – 537 pages
Description
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.
Reviews
I found the text enjoyable and easy to read. The authors provide a sufficient description of all the methodology for practical use. Each chapter includes at least one real world dataset analysis and the software commands summary tables included at the end of every chapter should be particularly helpful to a practitioner of statistics. … I would recommend the text for practitioners of statistics looking for a handy reference, particularly those performing basic analysis in the health sciences.
—Thomas J. Fisher, Journal of Biopharmaceutical Statistics, Issue 6, 2012
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
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.
Name: Practical Multivariate Analysis, Fifth Edition (Hardback) – Chapman and Hall/CRC
Description: By Abdelmonem Afifi, Susanne May, Virginia A. Clark. 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,...
Categories: Quantitative Methods, Regression Analysis and Multivariate Statistics, Statistics for the Biological Sciences, Statistical Theory & Methods
