How to Address Heteroskedasticity in Regression Models in R

These videos discuss and describe how to address heteroskedasticity in linear regression models in R. Part 1 discusses the consequences of heteroskedasticity and how to use a heteroskedasticity corrected covariance matrix (HCCM; "sandwich estimator") to estimate "robust" standard errors in an OLS regression model. Part 2 shows how to apply this approach when using maximum likelihood estimation, as well a potential alternative to HCCM (i.e., bootstrapping). Script File & Data: https://drive.google.com/drive/folder...