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19209656
Categorical Data Analysis (Wiley Series in Probability and Statistics)
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Item #:
19209656
BAM 207
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A must-have book for anyone expecting to do research and/or applications in categorical data analysis.
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What Stands Out
Comprehensive Coverage
This edition offers exhaustive insights into categorical data techniques, addressing both basic and advanced methods, which is essential for statisticians and data analysts seeking to deepen their understanding of statistical inference.
Real-World Applications
Incorporates practical examples and case studies, ensuring that readers can apply techniques effectively in real-world scenarios, making the subject matter accessible and relevant to practitioners and researchers alike.
Updated Content
The latest edition features refreshed content and new methodologies, showcasing contemporary approaches in categorical data analysis, helping users stay current with evolving statistical standards and tools.
Product Details
- Comprehensive treatment of methods for categorical data analysis in the biomedical, social sciences, and financial industries.
- Unified generalized linear models approach connecting logistic regression and Poisson and negative binomial loglinear models.
- New chapters on alternative methods for binary response data, smoothing and regularization methods, and classification methods like linear discriminant analysis and classification trees.
- Introduction of Bayesian approach for methods in a dedicated chapter.
- Supplementary website with examples using R, SAS, SPSS, and Stata, as well as exercise solutions.
- Authored by Alan Agresti, a Distinguished Professor Emeritus in the Department of Statistics at the University of Florida, who has extensive experience in presenting categorical data methods.
| Publisher | Wiley |
| Publication date | December 3, 2012 |
| Edition | 3rd |
| Language | English |
| Print length | 752 pages |
| ISBN-10 | 9780470463635 |
| ISBN-13 | 978-0470463635 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 1.5 x 10.1 inches (17.8 x 3.8 x 25.7 cm) |
Who Should Buy?
-
Statistical Researchers
Ideal for researchers conducting studies using categorical data and seeking comprehensive statistical methodologies and interpretations.
-
Graduate Students
Suitable for graduate students in statistics or related fields who need a solid understanding of categorical data analysis.
-
Data Analysts
Beneficial for data analysts looking to enhance their skills in analyzing and interpreting categorical datasets effectively.
-
Beginners
Not suitable for absolute beginners lacking foundational knowledge in statistics or data analysis techniques.
Product Description
Categorical Data Analysis (Wiley Series in Probability and Statistics)
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Probability & Statistics Editorial Review
Categorical Data Analysis (Wiley Series in Probability and Statistics) is an essential resource for statisticians and economists alike, providing a comprehensive exploration of statistical modeling for categorical data. The latest edition introduces enhanced chapters focusing on probit regression and novel modeling methods, expanding the scope for understanding Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM). Reviewers appreciate the clarity and practical examples included, which facilitate a better grasp of complex concepts like the Delta Method. Many users highlight the book's accessibility, even for those without extensive statistics backgrounds, making it an excellent choice for learners across disciplines.
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Pros
- Comprehensive coverage of GLM and GLMM topics
- User-friendly for beginners in statistics
- Includes practical examples and exercises
- Two additional chapters enhance understanding
- Access to online resources for further learning
Cons
- Some readers may find newer topics less rigorous than expected
Product Price History
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Features & Benefits
- Comprehensive treatment of most important methods for categorical data analysis
- Emphasis on logistic and probit regression methods
- Introduction of Bayesian approach for methods
- An invaluable tool for statisticians and methodologists
- More than 100 analyses of data sets and over 600 exercises
- Supplementary website showing how to use R and SAS
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