Content:

This course focuses on the estimation of models with discrete dependent variables, presenting a rigorous academic exploration of statistical methods and their application to real-world datasets. Through an examination of maximum likelihood estimation and various models, including linear regression, binary models (logit and probit), multinomial models, and count data analysis, students will gain a comprehensive understanding of the complexities inherent in analyzing discrete outcomes. Hands-on exercises using R software will provide practical experience in model implementation and interpretation, enabling students to conduct sophisticated data analysis with precision and confidence.

Table of contents:

• Introduction
• Maximum Likelihood
• Linear Models
• Binary Models
• Multinomial Models
• Models for Count Data
• Generalized Models for Discrete Outcomes

Examination:

 E-Examination

• Duration: 120 minutes
• Components
  • Estimation of three discussed models in R with provided data sets (75% of the final grade)
  • Interpretation and theoretical questions (25% of the final grade)

The examination will assess your proficiency in applying the discussed models using R, which constitutes the majority (75%) of the assessment. Additionally, questions will be designed to evaluate your ability to interpret results and engage with theoretical concepts, contributing to the remaining 25% of the overall grade.