Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
