Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.
Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
