by James M. Lattin, Paul E. Green and Doug Carroll
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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:
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: 978-0534349745Offering 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:
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