Practical Multivariate Analysis
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1 Texts in Statistical Science Practical Multivariate Analysis Fifth Edition Abdelmonem Afifi Susanne May Virginia A. Clark CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group an informa business A CHAPMAN & HALL BOOK
2 Contents Preface xv Authors' Biographies xix I Preparation for Analysis 1 1 What is multivariate analysis? Defining multivariate analysis Examples of multivariate analyses Multivariate analyses discussed in this book Organization and content of the book 12 2 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 Summary Problems 22 3 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 Summary Problems 45 vii
3 viii CONTENTS 4 Data screening and transformations Transformations, assessing normality and independence Common transformations Selecting appropriate transformations Assessing independence Summary Problems 64 5 Selecting appropriate analyses Which analyses to perform? Why selection is often difficult Appropriate statistical measures Selecting appropriate multivariate analyses Summary Problems 74 II Applied Regression Analysis 79 6 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: 6.7 Interpretation: fixed-x case 90 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 Summary Problems Multiple regression and correlation Chapter outline When are regression and correlation used? Data example Regression methods: fixed-.y case Regression and correlation: variable-x case 125
4 CONTENTS ix 7.6 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 Summary Problems 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 Summary Problems Special regression topics Chapter outline Missing values in regression analysis Dummy variables Constraints on parameters Regression analysis with multicollinearity Ridge regression Summary Problems 215 III 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 230
5 X CONTENTS 10.6 Discussion of computer programs What to watch out for Summary Problems 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 Summary Problems 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 Summary Problems Regression analysis with survival data Chapter outline When is survival analysis used? Data examples 324
6 CONTENTS xi 13.4 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 Summary Problems 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 Summary Problems 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 Summary Problems Cluster analysis Chapter outline When is cluster analysis used? Data example 407
7 16.4 Basic concepts: initial analysis 16.5 Analytical clustering techniques 16.6 Cluster analysis for financial data set 16.7 Discussion of computer programs 16.8 What to watch out for 16.9 Summary Problems 17 Log-linear analysis 17.1 Chapter outline 17.2 When is log-linear analysis used? 17.3 Data example 17.4 Notation and sample considerations 17.5 Tests and models for two-way tables 17.6 Example of a two-way table 17.7 Models for multiway tables 17.8 Exploratory model building 17.9 Assessing specific models Sample size issues The logit model Discussion of computer programs What to watch out for Summary Problems 18 Correlated outcomes regression Chapter outline 18.,2 When is correlated outcomes regression used? 18,.3 Data examples Basic concepts 18.5 Regression of clustered data 18.6 Regression of longitudinal data Other analyses of correlated outcomes 18.8 Discussion of computer programs 18.9 What to watch out for Summary Problems Appendix A A. 1 Data sets and how to obtain them A.2 Chemical companies financial data A.3 Depression study data
8 CONTENTS xiii A.4 Financial performance cluster analysis data 492 A.5 Lung cancer survival data 492 A.6 Lung function data 492 A.7 Parental HIV data 493 A. 8 Northridge earthquake data 493 A.9 School data 494 A. 10 Mice data 494 References 495 Index 509
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