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Applied Multivariate Statistical Analysis (6th Edition)
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What Stands Out
Product Details
| Publisher | Pearson |
| Publication date | April 2, 2007 |
| Edition | 6th |
| Language | English |
| Print length | 808 pages |
| ISBN-10 | 0131877151 |
| ISBN-13 | 978-0131877153 |
| Item Weight | 2.84 pounds (1.29 kg) |
| Dimensions | 7.2 x 1.3 x 9.3 inches (18.3 x 3.3 x 23.6 cm) |
Who Should Buy?
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Graduate Students
Ideal for graduate students in statistics or related fields needing comprehensive multivariate analysis concepts and applications.
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Data Analysts
Data analysts looking to deepen their understanding of multivariate techniques for practical data interpretation and modeling.
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Researchers
Researchers requiring advanced statistical methods for analyzing complex datasets and enhancing their empirical studies.
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Beginner Statisticians
Not suitable for beginners unfamiliar with basic statistical concepts, as it assumes prior knowledge and skills.
Product Description
Applied Multivariate Statistical Analysis (6th Edition)
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Probability & Statistics Editorial Review
This book is a comprehensive guide to multivariate statistical analysis but is geared towards those with a strong mathematical background. While it covers the basics of linear algebra, the book contains lengthy matrix math derivations and formulae that require manual calculation. The notation can be confusing, and the examples are scattered throughout the book without an index, making it challenging to follow. However, if you have taken calculus and linear algebra courses, this book is an excellent resource. It includes plenty of examples, and each chapter ends with a checklist and discusses how graphical tools can be used to assess statistical techniques' appropriateness. Overall, it is an essential resource for researchers, particularly those interested in multivariate linear regression, principal components, factor analysis, and clustering techniques.
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Pros
- Comprehensive guide to multivariate statistical analysis
- Includes plenty of examples and checklists
- Offers an excellent resource for researchers interested in multivariate linear regression, principal components, factor analysis, and clustering techniques
Cons
- Geared towards those with a strong mathematical background
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