Linear Regression: Basics and Interpretation

This lecture covers the basics of linear regression, focusing on understanding the model, predictors, outcomes, and coefficients. It explains how to find optimal coefficients for approximating outcomes as a linear function of predictors, the concept of least squares, and the use cases of regression for prediction, descriptive data analysis, and causal modeling. The lecture also delves into interpreting fitted parameters, including the intercept and slope, with examples involving binary and continuous predictors. It discusses assumptions in regression modeling, transformations of predictors and outcomes, and techniques like mean-centering and standardization. Additionally, it explores logarithmic outcomes, quantifying uncertainty, and goes beyond comparing means by introducing the concept of 'Difference in Differences' for causal analysis.