on $25 & up

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

ISBN13: 978-0534349745

ISBN10: 0534349749

Cover type:

Edition: 03

Copyright: 2003

Publisher: Duxbury Press

Published: 2003

International: No

ISBN10: 0534349749

Cover type:

Edition: 03

Copyright: 2003

Publisher: Duxbury Press

Published: 2003

International: No

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

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

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

ISBN13: 978-0534349745ISBN10: 0534349749

Cover type:

Edition: 03

Copyright: 2003

Publisher: Duxbury Press

Published: 2003

International: No

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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