Analyzing Multivariate Data [With CDROM]

Lattin; Green, Paul E., Jr.; Lattin, James M.

ISBN 10: 0534349749 / ISBN 13: 9780534349745

Verlag: Cengage Learning, 2002

Beschreibung

May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 2.3. Bestandsnummer des Verkäufers G0534349749I4N00

Bibliografische Details

Titel: Analyzing Multivariate Data [With CDROM]

Verlag: Cengage Learning

Erscheinungsdatum: 2002

Einband: Hardcover

Zustand: Very Good

Zustand des Schutzumschlags: No Jacket

Über diesen Titel

Inhaltsangabe:

Book by Lattin James Carroll Douglas Green Paul

Críticas:

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.