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Analyzing Multivariate Data / With CD

Analyzing Multivariate Data / With CD - 03 edition

ISBN13: 978-0534349745

Cover of Analyzing Multivariate Data / With CD 03 (ISBN 978-0534349745)
ISBN13: 978-0534349745
ISBN10: 0534349749
Cover type: Hardback
Edition/Copyright: 03
Publisher: Duxbury Press
Published: 2003
International: No

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Analyzing Multivariate Data / With CD - 03 edition

ISBN13: 978-0534349745

James M. Lattin, Paul E. Green and Doug Carroll

ISBN13: 978-0534349745
ISBN10: 0534349749
Cover type: Hardback
Edition/Copyright: 03
Publisher: Duxbury Press

Published: 2003
International: No
Summary

Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Green, and Carroll have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not yet advanced statistics majors. Their text accomplishes this through a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through geometric presentation. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with matrix algebra.

Benefits:

  • Uses geometric interpretation to develop readers' intuition and provide students with a mental picture of how each method works. Mathematics is used to support the underlying intuition.
  • Takes a pragmatic, hands-on approach through sample problems based on real data. Each chapter offers at least one application, as well as a discussion of issues related to the proper interpretation of the results.
  • Accompanying student workbooks are specific to a given software package (either SAS or SPSS), and annotated program output facilitates interpretation and provides links to concepts included in the text.
  • Addresses important issues that come up with each application of a method. Special emphasis is placed on generalizing the results of the analysis, and suggestions are presented for testing the validity of findings.
  • Contains illustrations and sample problems from a wide range of areas, including psychology, sociology, and marketing research.
  • Follows a standard format in each chapter. This format begins by discussing a general set of research objectives, followed by some illustrative examples of problems in different areas. Then it provides an explanation of how each methods works, followed by a sample problem, application of the technique, and interpretation of results.

Author Bio

Lattin, Jim : Stanford University


Carroll, Doug : Rutgers University


Green, Paul : University of Pennsylvania

Table of Contents

1. INTRODUCTION

The Nature of Multivariate Data
Overview of Multivariate Methods
Format of Succeeding Chapters

2. VECTORS AND MATRIXES

Introduction
Definitions
Geometric Interpretation of Operations
Matrix Properties
Learning Summary
Exercises

3. REGRESSION ANALYSIS

Introduction
Regression Analysis: How it Works
Sample Problem: Leslie Salt Property
Learning Summary
Exercises

4. PRINCIPAL COMPONENTS ANALYSIS

Introduction
Principal Components: How it Works
Sample Problem: Gross State Production
Questions Regarding the Application of Principal Components
Learning Summary
Exercises

5. EXPLORATORY FACTOR ANALYSIS

Introduction
Exploratory Factor Analysis: How it Works
Sample Problem: Perceptions of Ready-to-Eat Cereals
Questions Regarding the Application of Factor Analysis
Learning Summary
Exercises

6. CONFIRMATORY FACTOR ANALYSIS

Introduction
Confirmatory Factor Analysis: How Does it Work? Sample Problems
Questions Regarding the Application of Confirmatory Factor Analysis
Learning Summary
Exercises

7. MULTIDIMENSIONAL SCALING

Introduction
Metric MDS: How Does it Work? Non-Metric MDS: How Does it Work? Individual Differences Scaling: How Does It Work? Centroid Scaling: How Does it Work? A Note on Model Validation
Learning Summary
Exercises

8. CLUSTERING

Introduction
Objectives of Cluster Analysis
Measures of Distance, Dissimilarity, and Density
Agglomerative Clustering: How IT Works
Partitioning: How it Works
Sample Problem: Preference Segmentation
Questions Regarding the Application of Cluster Analysis
Learning Summary
Exercises

9. CANONICAL CORRELATION

Introduction
Canonical Correlation: How Does it Work? Sample Problem
Questions Regarding the Application of Canonical Correlation
Learning Summary
Exercises

10. STRUCTURAL EQUATION MODELS WITH LATENT VARIABLES

Introduction
Structural Equations with Latent Variables: How Does it Work? Sample Problem: Modeling the Adoption of Innovation
Questions Regarding the Application of Structural Equations with Latent Variables
Learning Summary
Exercises

11. ANALYSIS OF VARIANCE

Introduction
ANOLVA and ANCOVA: How Does it Work? Sample Problem: Test Marketing a New Product
Multiple Analysis of Variance (MANOVA): How Does it Work
Sample Problem: Testing Advertising Message Strategy
Questions Regarding the Application of MANOVA and MANCOVA
Learning Summary
Exercises

12. DISCRIMINANT ANALYSIS

Introduction
Two-Group Discriminant Analysis: How Does it Work? Sample Problem: Book Club Data
Questions Regarding the Application of Two-Group Discriminant Analysis
Multiple Discriminant Analysis: How Does it Work? Sample Problem: Real Estate
Questions Regarding the Application of Multiple Discriminant Analysis
Learning Summary
Exercises

13. LOGIT CHOICE MODELS

Introduction
Binary Logit Model: How Does it Work? Sample Problem: Books Direct
Multinomial Logit Model: How Does it Work? Sample Problem: Brand Choice
Questions Regarding the Application of Logit Choice Models
Learning Summary
Exercises

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