Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS
This volume delves into the theory and SAS-based applications of generalized linear and nonlinear models tailored for correlated data. It equips statisticians and analysts with tools to address dependencies in datasets, enhancing model reliability and interpretability in diverse research contexts.
About This Book
Generalized Linear and Nonlinear Models for Correlated Data provides a detailed examination of statistical techniques for handling correlated observations. The text covers theoretical aspects essential for understanding model structures and assumptions.
Applications are demonstrated through SAS software, enabling readers to implement models in real-world scenarios. This approach bridges theory and practice for effective data analysis.
The book is structured to support researchers and practitioners working with complex datasets. It emphasizes methods that account for dependencies in data, improving accuracy in predictions and inferences.
Overall, it offers a solid foundation for advanced statistical modeling, particularly in fields involving longitudinal or clustered data.
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