Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.
Discusses Monte Carlo VaR accuracy, confidence intervals, backtesting, and multivariate distributions.
Explores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.
