Regression Analysis: Disentangling Data

This lecture covers regression analysis for disentangling data, focusing on linear regression modeling. The instructor explains the assumptions, transformations, and interpretations of coefficients in regression models. Topics include mean-centering predictors, standardization via z-scores, and transformations of predictors and outcomes. The lecture also introduces generalized linear models, beyond linear regression, for comparing means and exploring causality using difference in differences. Practical examples and the importance of model specification and diagnostics are highlighted.

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