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  4. Analysis of Discrete Data

Analysis of Discrete Data

Objectives

Gain familiarity and understand the main tools to deal with discrete data using R.

General characterization

Code

200198

Credits

4.0

Responsible teacher

Bruno Miguel Pinto Damásio

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Matrix algebra and statistics (recommended)

Bibliography

Teaching method

  • The theoretical classes aim to provide the student with the theoretical background for each topic.
  • The practical classes aim to apply the concepts and methodologies learned in the theoretical classes

Evaluation method

The final grade depends on the project grade (PG) throughout the semester and the final exam (FE) (first and second season).

FG = max{0.4PG + 0.6FE; FE}

The formula above implies the following: if the final exam grade is higher than the project grade, the final grade is given exclusively by the final exam grade. If not, the final grade is given by the formula 0.4PG + 0.6FE. 

Subject matter

1. Introduction: Distributions and Inference for Categorical Data

2. Analyzing Contingency Tables

3. Generalized Linear Models

4. Logistic Regression Models