This course focuses on estimating 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. 

1. Introduction

2. Maximum Likelihood

3. Linear Models

4. Binary Models

5. Multinomial Models

6. Models for Count Data

7. Generalized Models for Discrete Outcomes