Linear Regression Analysis

This lecture covers the fundamentals of linear regression analysis, focusing on model building, predictors, coefficients, and outcome interpretation. It explains the process of finding optimal coefficients for approximating outcomes as a linear function of predictors. The lecture also delves into the comparison of mean outcomes, interpretation of fitted parameters, and examples with binary and continuous predictors. Additionally, it discusses the importance of quantifying uncertainty, transformations of predictors and outcomes, and going beyond linear regression for causal modeling. The instructor emphasizes the significance of model assumptions, mean-centering, standardization, and logarithmic outcomes in regression analysis.