## Statistical Models: Theory and Practice

By David A. Freedman

Category

MathRecommended by

"Statistical Models" by David A. Freedman provides a comprehensive and accessible guide to statistical modeling. This indispensable resource covers the most commonly used statistical models, including linear regression, logistic regression, and survival analysis.

Freedman explains the principles and techniques behind statistical modeling, offering clear explanations and real-world examples. The book emphasizes the importance of careful model selection and interpretation, providing practical advice on addressing common pitfalls and challenges.

Readers will learn how to formulate models, estimate model parameters, and assess model fit through hypothesis testing and model diagnostics. Freedman emphasizes the role of subject expertise and emphasizes the need for critical thinking when interpreting statistical results.

The book also covers advanced topics such as hierarchical models, generalized additive models, and mixed-effects models. Throughout the text, the author emphasizes the importance of understanding assumptions and limitations, enabling readers to make informed decisions when selecting and using statistical models.

"Statistical Models" is suitable for both beginners and more advanced practitioners in statistics and related fields. Its clear and concise style makes it an excellent reference for anyone looking to gain a solid understanding of statistical modeling and its applications.

Freedman explains the principles and techniques behind statistical modeling, offering clear explanations and real-world examples. The book emphasizes the importance of careful model selection and interpretation, providing practical advice on addressing common pitfalls and challenges.

Readers will learn how to formulate models, estimate model parameters, and assess model fit through hypothesis testing and model diagnostics. Freedman emphasizes the role of subject expertise and emphasizes the need for critical thinking when interpreting statistical results.

The book also covers advanced topics such as hierarchical models, generalized additive models, and mixed-effects models. Throughout the text, the author emphasizes the importance of understanding assumptions and limitations, enabling readers to make informed decisions when selecting and using statistical models.

"Statistical Models" is suitable for both beginners and more advanced practitioners in statistics and related fields. Its clear and concise style makes it an excellent reference for anyone looking to gain a solid understanding of statistical modeling and its applications.

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