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Updated A.Y. 2021-2022

Attending students from the B.D. in Business Administration and Economics must have passed Quantitative Methods I and Quantitative Methods II (or similar courses). A good understanding of multiple linear regression models is mandatory.

Detailed of topics:

  • Introduction to the R software
  • Categorical data analysis: multi-way tables, distributions.
  • Principles of causal analysis: selection, bias, confounding. Confounders, colliders, mediators. Simpson's Paradox
  • Binary outcome regression models: Linear probability model, Logit and Probit.
  • Multinomial ouctome regression: Multinomial Logit.
  • Count outcome regression models: Poisson regression and Overdispersion.
  • Instrumental Variables.
  • Panel data models and estimators.
  • Introduction to supervised learning: classification trees and random forests.