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Home > Mathematics and Science Textbooks > Mathematics > Probability and statistics > Analyzing Multivariate Data: (Duxbury Applied Series)
Analyzing Multivariate Data: (Duxbury Applied Series)

Analyzing Multivariate Data: (Duxbury Applied Series)

          
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About the Book

Offering the latest teaching and practice of applied multivariate statistics, this text is designed for students who need an applied introduction to the subject. Lattin, Green and Carroll have created a text that addresses 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 provide illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with matrix algebra.

Table of Contents:
Part One: OVERVIEW. 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. Part Two: ANALYSIS OF INTERDEPENDENCE. 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. Part Three: ANALYSIS OF DEPENDENCE. 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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Product Details
  • ISBN-13: 9780534349745
  • Publisher: Cengage Learning, Inc
  • Publisher Imprint: Brooks/Cole
  • Depth: 25
  • Language: English
  • Returnable: N
  • Spine Width: 26 mm
  • Width: 238 mm
  • ISBN-10: 0534349749
  • Publisher Date: 03 Dec 2002
  • Binding: SA
  • Height: 190 mm
  • No of Pages: 556
  • Series Title: Duxbury Applied Series
  • Weight: 1060 gr


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