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Publisher:

Chapman & Hall/CRC

Publication Date:

2011

Number of Pages:

517

Format:

Hardcover

Edition:

5

Series:

Texts in Statistical Science

Price:

89.95

ISBN:

9781439816806

Category:

Textbook

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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

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