A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
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The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, New York Times, Slate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, and Data Analysis and Regression Using Multilevel/Hierarchical Models. Andrew Gelman is Higgins Professor of Statistics and Professor of Political Science at Columbia University.
Jennifer Hill is Professor of Applied Statistics at New York University.
Aki Vehtari is Associate Professor in Computational Probabilistic Modeling at Aalto University, Finland.
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