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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, Carroll, and Green have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not advanced statistics majors. The text provides a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through applications. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with vectors and matrix algebra. Additionally, each chapter follows a standard format. This format begins by discussing a general set of research objectives, followed by illustrative examples of problems in different areas. Then it provides an explanation of how each method works, followed by a sample problem, application of the technique, and interpretation of results.
- Sales Rank: #213241 in Books
- Brand: Duxbury Press
- Published on: 2002-12-03
- Original language: English
- Number of items: 1
- Dimensions: 1.03" h x 7.92" w x 9.04" l, 2.24 pounds
- Binding: Hardcover
- 580 pages
- Used Book in Good Condition
Review
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.
Most helpful customer reviews
7 of 7 people found the following review helpful.
The first statistics book i've ever read for pleasure...
By Cyanide Bunny
I bought this book as a cheap international edition because, er, it was cheap. It turned out to be an excellent reference for understanding many useful concepts in multivariate analysis, and as another reviewer said, it clarifies many of the ideas with real examples. Because the style is so accessible, I ended up reading it as much out of interest as the need to learn about all the techniques. It lacks the rigor of some other texts, but there are plenty of them out there to refer to for more concrete proofs if you need them. Read this to understand the techniques...
4 of 4 people found the following review helpful.
In depth, but easy to understand.
By Genevieve Hayes
"Analyzing Multivariate Data" is not just one the of the best multivariate data analysis texts available, but also one of the best structured text books that I have ever seen. After providing an introductory discussion of the prerequisite materials (namely matrices and regression analysis), Lattin, Carroll and Green cover all of the expected areas of multivariate analysis, specifically: principal components analysis, factor analysis, multidimensional scaling, cluster analysis, canonical correlation, structural equation models, analysis of variance, discriminant analysis and logit choice models. What makes this book stand out from other, similar, text books is how the material is structured. In each chapter, the authors explain all of the relevant mathematics behind the techniques, but also explain the concepts in laymans terms. Important questions are posed and answered, such as "when should this technique be used?" and worked examples are given. Furthermore, at the end of each chapter, the main points are summarized in point form and a large number of exercises are provided. The only drawback I can see is that the solutions to the exercises are not provided.
Even though this text covers material at a reasonably high level (this book would be suitable for later year undergraduates or graduate students), this is still an extremely user-friendly textbook. I just wish more texts were this well written.
8 of 10 people found the following review helpful.
Excellent bridge between practical use and rigor.
By BlueDaisy
Analyzing Multivariate Data by Lattin, Carroll, and Green fills an important niche in practical statistics for the applied researcher. For a purely conceptual introduction, Sam Kash Kachigan (Multivariate Statistical Analysis) or Joseph Hair et al (Multivariate Data Analysis) are excellent references; for more rigorously mathematical approaches, Johnson & Wichern's and Donald Morrison's texts are excellent. Analyzing Multivariate Data bridges those two poles. The rigorous texts teach how to twirl matrices around like a flaming baton, but unless the researcher has insight into the art and science of the applications of the multivariate methods little will be understood. This is particularly important if the researcher uses point-and-click stat packages. For the applied researcher and grad student who want to know how and when to use the multivariate methods but want accessible supporting mathematics as well, this book is exceptional. I use it to explain the applications of the mathematically intensive methods I am learning in a grad multivariate class.
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