Analyzing Multivariate Data

Name: Analyzing Multivariate Data
Availability: OutOfStock
ISBN: 9780534462420

ISBN-10: 0534462421

ISBN-13: 9780534462420

Edition: 2003

Authors: James M. Lattin, Paul E. Green, Douglas Carroll

List price: $55.95

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Offering the latest teaching and practice of applied multivariate statistics, this text is designed for students who need an applied introduction to the subject.

Book details

List price: $55.95
Copyright year: 2003
Publisher: Brooks/Cole
Publication date: 12/6/2002
Binding: Digital, Other
Pages: 556
Size: 5.25" wide x 7.00" long x 0.10" tall
Weight: 4.994
Language: English




Overview



Introduction


The Nature of Multivariate Data


Overview of Multivariate Methods


Format of Succeeding Chapters



Vectors and Matrixes


Introduction


Definitions


Geometric Interpretation of Operations


Matrix Properties


Learning Summary


Exercises



Analysis of Interdependence



Regression Analysis


Introduction


Regression Analysis: How it Works


Sample Problem: Leslie Salt Property


Learning Summary


Exercises



Principal Components Analysis


Introduction


Principal Components: How it Works


Sample Problem: Gross State Production


Questions Regarding the Application of Principal Components


Learning Summary


Exercises



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



Confirmatory Factor Analysis


Introduction


Confirmatory Factor Analysis: How Does it Work? Sample Problems


Questions Regarding the Application of Confirmatory Factor Analysis


Learning Summary


Exercises



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



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



Analysis of Dependence



Canonical Correlation


Introduction


Canonical Correlation: How Does it Work? Sample Problem


Questions Regarding the Application of Canonical Correlation


Learning Summary


Exercises



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



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



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



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