In "Statistical Models," David Freedman presents a comprehensive exploration of statistical methods crucial for interpreting research across the social and health sciences. With a clear and engaging narrative, Freedman breaks down complex concepts of association, regression, and causality into digestible parts, making this textbook an essential tool for anyone looking to navigate the intricate world of empirical papers. Through a detailed examination of linear models, including generalized least squares and probits and logits for binary variables, as well as an introduction to the bootstrap method for estimating bias and computing standard errors, Freedman equips readers with the necessary skills to not only understand but also build their statistical models. What sets this book apart is its practical approach, as Freedman meticulously connects theoretical statistical methods to real-world applications. Each chapter is structured around published studies, providing a solid foundation for understanding how statistical modeling applies to actual research. The inclusion of a wide range of exercises, many with answers, further enhances the learning experience, allowing readers to test their knowledge and apply what they have learned. Whether you are an advanced undergraduate, a beginning graduate student in statistics, or a professional in the social and health sciences, "Statistical Models" offers invaluable guidance on the principles of modeling and statistical inference, ensuring you are well-prepared to tackle the challenges of empirical research.