# Estatística para a Ciência de Dados

## Objetivos

The objective of data analysis is to extract the relevant information from the data, which can then be used to solve a given problem. The purpose of this course is to provide and solid background in exploratory data analysis and statistical inference.

## Caracterização geral

200178

7.5

##### 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

Statistics and linear algebra (recommended)

### Bibliografia

• Lock, Robin, H., Lock, P. F., Morgan, K.L., Lock, E.F.; Lock, D.F. (2017) Statistics: unlocking the power of data. Second edition, Wiley.
• Wooldridge, J. M., Introductory econometrics: A modern approach, 6th Edition. South-Western, Cengage Learning, 2016;
• Heiss, F., Using \textbf{R} for Introductory Econometrics, 1st Edition; CreateSpace (Independent publishing platform), 2016.
• Greene, W. H., Econometric analysis, 7th edition, Pearson, 2012;
• Stock, J. H and Watson, M. W, Introduction to Econometrics, 3rd. Edition, Pearson, Addison Wesley, 2011

### Método de ensino

The course is based on theoretical-practical and practical classes. Practical classes and problem-solving oriented.

### Método de avaliação

• (50%) Final exam (1st and 2nd rounds)
• (50%) Project (optional)

A minimum grade of 8.5 is required in the final exam to pass.

## Conteúdo

1. Collecting data
2. Statistical distributions
3. Describing data
4. Confidence intervals
5. Hypothesis testing
6. Inference
7. The Nature of Econometrics. Correlation vs Causality
8. The Multiple Linear Regression Model (MLRM)
9. MLRM: Inference
10. Heteroskedasticity
11. Asymptotic properties of the OLS