Preface

Author’s Biographies

I. Preparation for Analysis

1. What is multivariate analysis?

1.1 Defining multivariate analysis

1.2 Examples of multivariate analyses

1.3 Multivariate analyses discussed in this book

1.4 Organization and content of the book

2. Characterizing data for analysis

2.1 Variables: their definition, classification, and use

2.2 Defining statistical variables

2.3 Stevens’s classification of variables

2.4 How variables are used in data analysis

2.5 Examples of classifying variables

2.6 Other characteristics of data

2.7 Summary

2.8 Problems

3. Preparing for data analysis

3.1 Processing data so they can be analyzed

3.2 Choice of a statistical package

3.3 Techniques for data entry

3.4 Organizing the data

3.5 Example: depression study

3.6 Summary

3.7 Problems

4. Data screening and transformations

4.1 Transformations, assessing normality and independence

4.2 Common transformations

4.3 Selecting appropriate transformations

4.4 Assessing independence

4.5 Summary

4.6 Problems

5. Selecting appropriate analyses

5.1 Which analyses to perform?

5.2 Why selection is often difficult

5.3 Appropriate statistical measures

5.4 Selecting appropriate multivariate analyses

5.5 Summary

5.6 Problems

II. Applied Regression Analysis

6. Simple regression and correlation

6.1 Chapter outline

6.2 When are regression and correlation used?

6.3 Data example

6.4 Regression methods: fixed-*X* case

6.5 Regression and correlation: variable-*X* case

6.6 Interpretation: fixed-*X* case

6.7 Interpretation: variable-*X* case

6.8 Other available computer output

6.9 Robustness and transformations for regression

6.10 Other types of regression

6.11 Special applications of regression

6.12 Discussion of computer programs

6.13 What to watch out for

6.14 Summary

6.15 Problems

7. Multiple regression and correlation

7.1 Chapter outline

7.2 When are regression and correlation used?

7.3 Data example

7.4 Regression methods: fixed-*X* case

7.5 Regression and correlation: variable-*X* case

7.6 Interpretation: fixed-*X* case

7.7 Interpretation: variable-*X* case

7.8 Regression diagnostics and transformations

7.9 Other options in computer programs

7.10 Discussion of computer programs

7.11 What to watch out for

7.12 Summary

7.13 Problems

8. Variable selection in regression

8.1 Chapter outline

8.2 When are variable selection methods used?

8.3 Data example

8.4 Criteria for variable selection

8.5 A general *F* test

8.6 Stepwise regression

8.7 Subset regression

8.8 Discussion of computer programs

8.9 Discussion of strategies

8.10 What to watch out for

8.11 Summary

8.12 Problems

9. Special regression topics

9.1 Chapter outline

9.2 Missing values in regression analysis

9.3 Dummy variables

9.4 Constraints on parameters

9.5 Regression analysis with multicollinearity

9.6 Ridge regression

9.7 Summary

9.8 Problems

III. Multivariate Analysis

10. Canonical correlation analysis

10.1 Chapter outline

10.2 When is canonical correlation analysis used?

10.3 Data example

10.4 Basic concepts of canonical correlation

10.5 Other topics in canonical correlation

10.6 Discussion of computer programs

10.7 What to watch out for

10.8 Summary

10.9 Problems

11. Discriminant analysis

11.1 Chapter outing

11.2 When is discriminant analysis used?

11.3 Data example

11.4 Basic concepts of classification

11.5 Theoretical background

11.6 Interpretation

11.7 Adjusting the dividing point

11.8 How good is the discrimination?

11.9 Testing variable contributions

11.10 Variable selection

11.11 Discussion of computer programs

11.12 What to watch out for

11.13 Summary

11.14 Problems

12. Logistic regression

12.1 Chapter outline

12.2 When is logistic regression used?

12.3 Data example

12.4 Basic concepts of logistic regression

12.5 Interpretation: categorical variables

12.6 Interpretation: continuous variables

12.7 Interpretation: interactions

12.8 Refining and evaluating logistic regression

12.9 Nominal and ordinal logistic regression

12.10 Applications of logistic regression

12.11 Poisson regression

12.12 Discussion of computer programs

12.13 What to watch out for

12.14 Summary

12.15 Problems

13. Regression analysis with survival data

13.1 Chapter outline

13.2 When is survival analysis used?

13.3 Data examples

13.4 Survival functions

13.5 Common survival distributions

13.6 Comparing survival among groups

13.7 The log-linear regression model

13.8 The Cox regression model

13.9 Comparing regression models

13.10 Discussion of computer programs

13.11 What to watch out for

13.12 Summary

13.13 Problems

14. Principal components analysis

14.1 Chapter outline

14.2 When is principal components analysis used?

14.3 Data example

14.4 Basic concepts

14.5 Interpretation

14.6 Other uses

14.7 Discussion of computer programs

14.8 What to watch out for

14.9 Summary

14.10 Problems

15. Factor analysis

15.1 Chapter outline

15.2 When is factor analysis used?

15.3 Data example

15.4 Basic concepts

15.5 Initial extraction: principal components

15.6 Initial extraction: iterated components

15.7 Factor rotations

15.8 Assigning factor scores

15.9 Application of factor analysis

15.10 Discussion of computer programs

15.11 What to watch out for

15.12 Summary

15.13 Problems

16. Cluster analysis

16.1 Chapter outline

16.2 When is cluster analysis used?

16.3 Data example

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

16.10 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

17.10 Sample size issues

17.11 The logit model

17.12 Discussion of computer programs

17.13 What to watch out for

17.14 Summary

17.15 Problems

18. Correlated outcomes regression

18.1 Chapter outline

18.2 When is correlated outcomes regression used?

18.3 Data examples

18.4 Basic concepts

18.5 Regression of clustered data

18.6 Regression of longitudinal data

18.7 Other analyses of correlated outcomes

18.8 Discussion of computer programs

18.9 What to watch out for

18.10 Summary

18.11 Problems

19. Appendix A

A.1 Data sets and how to obtain them

A.2 Chemical companies financial data

A.3 Depression study data

A.4 Financial performance cluster analysis data

A.5 Lung cancer survival data

A.6 Lung function data

A.7 Paternal HIV data

A.8 Northridge earthquake data

A.9 School data

A.10 Mice data

References

Index