1. Início
  2. NOVA Information Management School
  3. Pós-Graduação em Sistemas de Informação Empresariais
  4. Análise de Dados Discretos

Análise de Dados Discretos

Objetivos

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

Caracterização geral

Código

200198

Créditos

4.0

Professor responsável

Bruno Miguel Pinto Damásio

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

Matrix algebra and statistics (recommended)

Bibliografia

Método de ensino

  • 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

Método de avaliação

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. 

Conteúdo

1. Introduction: Distributions and Inference for Categorical Data

2. Analyzing Contingency Tables

3. Generalized Linear Models

4. Logistic Regression Models