### What's included

## Overview

*This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit **www.pearsonhighered.com/math-classics-series** for a complete list of titles.*

*For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics.*

* *

*Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.*

## Table of contents

**DRAFT**

(NOTE: *Each chapter begins with an Introduction, and concludes with Exercises and References.*)

**I. GETTING STARTED.**

**1. Aspects of Multivariate Analysis.**

**2. Matrix Algebra and Random Vectors.**

**3. Sample Geometry and Random Sampling.**

**4. The Multivariate Normal Distribution.**

**S**. Large-Sample Behavior of `X and

**S**. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality.

**II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.**

**5. Inferences About a Mean Vector.**

*T*

^{2}and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the

*p*-Dimensional Ellipsoids.

**6. Comparisons of Several Multivariate Means.**

**7. Multivariate Linear Regression Models.**

**III. ANALYSIS OF A COVARIANCE STRUCTURE.**

**8. Principal Components.**

**9. Factor Analysis and Inference for Structured Covariance Matrices.**

**10. Canonical Correlation Analysis**

**IV. CLASSIFICATION AND GROUPING TECHNIQUES.**

**11. Discrimination and Classification.**

**12. Clustering, Distance Methods and Ordination.**

**Appendix.**

*t*-Distribution Percentage Points. …c2 Distribution Percentage Points.

*F*-Distribution Percentage Points.

*F*-Distribution Percentage Points (…a = .10).

*F*-Distribution Percentage Points (…a = .05).

*F*-Distribution Percentage Points (…a = .01).

**Data Index.**

**Subject Index.**

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