Applied Multivariate Statistical Analysis 6e
Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations.
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Applied Multivariate Statistical Analysis 6e
Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
Features:
■ Accessible Level:
- Presents the concepts and methods of multivariate analysis at a level that is readily understandable by readers who have taken two or more statistics courses.
- Emphasizes the applications of multivariate methods and, consequently, they have made the mathematics as palatable as possible. The use of calculus is
avoided.
■ Organization and Approach:
- Contains the methodological tools of multivariate analysis in chapters 5 through 12.
- The approach in the methodological chapters (chapters 5-12) is to keep the discussion direct and uncluttered.
■ An abundance of examples and exercises based on real data. It includes, in some cases, snapshots of the corresponding SAS output.
■ Targeted Presentation of Key Concepts:
- Directs students’ attention to essential material
■ Emphasis on applications of multivariate methods.
■ A clear and insightful explanation of multivariate techniques
Contents:
I. Getting Started
1. Aspects of Multivariate Analysis.
2. Sample Geometry and Random Sampling.
3. Matrix Algebra and Random Vectors.
4. The Multivariate Normal Distribution.
II. Inferences about Multivariate Means and Linear Models
5. Inferences About a Mean Vector.
6. Comparisons of Several Multivariate Means.
7. Multivariate Linear Regression Models.
III. Analysis of a Covariance Structure
8. Principal Components.
9. Factor Analysis and Inference for Structured Covariance Matrices.
10. Canonical Correlation Analysis
IV. Classification and Grouping Techniques
11. Discrimination and Classification.
12. Clustering, Distance Methods and Ordination
| Book | |
|---|---|
| Author | Johnson / Wichern |
| Pages | 776 |
| Year | 2015 |
| ISBN | 9789332549555 |
| Publisher | Pearson |
| Language | English |
| Uncategorized | |
| Edition | 6/e |
| Weight | 940 g |
| Dimensions | 20.3 x 25.4 x 4.7 cm |
| Binding | Paperback |