- Assumptions for linear regression
- Regression inference assuming predictors are fixed
- Linear regression with random regressors, part 2
- The robust sandwich variance estimator for linear regression (theory)
- R squared and adjusted R squared
- R squared and goodness of fit in linear regression
- The robust sandwich variance estimator for linear regression (using R)
- Interval regression with heteroskedastic errors
- R squared/correlation depends on variance of predictor
- The mean of residuals in linear regression is always zero