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#20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari

Learning Bayesian Statistics

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Understanding Linear Regression Assumptions

This chapter explores the foundational assumptions of linear regression, particularly the critical role of linearity and its implications for interpreting coefficients and making predictions. It emphasizes the necessity of careful modeling and appropriate transformations of data to ensure meaningful analysis in various research contexts. The discussion also highlights the importance of tailored study designs and active learning methods to effectively teach complex concepts in causal inference.

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