Join Andrew Gelman, a statistics and political science professor at Columbia, Jennifer Hill from NYU specializing in causal questions, and Aki Vehtari, an expert in computational modeling from Aalto University, as they dive into the enchanting world of regression analysis. They share insights on their writing journey, offer ten tips to enhance regression modeling, tackle the challenges of statistical significance, and reveal the power of storytelling in data education. Plus, there's a whimsical discussion about exploring Mars!

Professor Jennifer Hill from NYU discusses causality, correlation vs. causation, counterfactuals, Bayesian and ML tools for causal inferences, and a new GUI for causal inferences. Tips on learning more about causal inference, the usefulness of multilevel models, and clarifying assumptions when inferring causality from data are also covered.