Statistical Models Theory And Practice Freedman Pdf
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Review of Statistical Models: Theory and Practice by David A. Freedman
Statistical models are widely used in various fields of science, engineering, and social sciences. They are tools for describing data, testing hypotheses, and making predictions. However, not all models are equally valid or useful. In this book, David A. Freedman, a renowned statistician and professor at the University of California, Berkeley, provides a critical and accessible introduction to the theory and practice of statistical modeling.
The book covers topics such as regression, analysis of variance, logistic regression, causal inference, and model selection. It also discusses the limitations and pitfalls of statistical modeling, such as overfitting, confounding, selection bias, and extrapolation. Freedman emphasizes the importance of understanding the assumptions and logic behind each model, as well as checking its fit and accuracy with data. He also illustrates his points with real-world examples and case studies from various disciplines.
The book is intended for students and practitioners who have some background in mathematics and statistics, but not necessarily in advanced calculus or linear algebra. It is suitable for undergraduate and graduate courses in statistics, as well as for self-study or reference. The book is available in PDF format for free download from PDF Room, a website that provides access to thousands of books in various fields.
One of the main themes of the book is the distinction between association and causation. Freedman explains that statistical models can only show correlations between variables, but not causal relationships. To establish causation, one needs to use experimental methods or natural experiments, where the effects of potential confounders are controlled or randomized. He also warns against the misuse of statistical models to support causal claims that are not justified by the data or the theory.
Another theme of the book is the role of judgment and common sense in statistical modeling. Freedman argues that statistical models are not objective or mechanical, but rather depend on the choices and assumptions of the modeler. He advises that modelers should be aware of their own biases and preferences, and be open to alternative explanations and perspectives. He also suggests that modelers should consult domain experts and use substantive knowledge to guide their modeling decisions.
The book is written in a clear and engaging style, with a balance of rigor and intuition. Freedman uses examples and exercises to illustrate the concepts and methods, and provides solutions and hints at the end of each chapter. He also includes references and suggestions for further reading for those who want to explore more advanced topics. The book is a valuable resource for anyone who wants to learn more about statistical models and how to use them wisely and responsibly. aa16f39245