Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
