Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.
Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.
Delves into regression analysis, emphasizing linear predictors' role in approximating outcomes and discussing generalized linear models and causal inference techniques.
Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.
