Generalized Linear Models With Examples in R (Springer Texts in Statistics)
by Peter K. Dunn, Gordon K. Smyth
This book provides a thorough introduction to generalized linear models, building on linear regression to handle varied data distributions. Through detailed explanations and R-based examples, it guides readers in model selection, estimation, and inference. Ideal for statistics students and researchers seeking practical statistical modeling skills.
About This Book
Generalized linear models extend classical linear regression to accommodate response variables that follow non-normal distributions, such as binary, count, or continuous data with specific variance structures.
The book presents these models systematically, covering theoretical foundations alongside computational aspects using R, a widely used open-source software for statistical computing.
Examples throughout the text illustrate model fitting, interpretation, and diagnostics, making complex concepts accessible to students and practitioners in statistics.
As part of the Springer Texts in Statistics series, it serves as a comprehensive resource for learning GLM applications in real-world scenarios.
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