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
Código
200178
Créditos
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)
- Final grade: maximum (Final exam grade;0.5*(Final exam grade)+0.5*Project grade))
A minimum grade of 8.5 is required in the final exam to pass.
Conteúdo
- Collecting data
- Statistical distributions
- Describing data
- Confidence intervals
- Hypothesis testing
- Inference
- The Nature of Econometrics. Correlation vs Causality
- The Multiple Linear Regression Model (MLRM)
- MLRM: Inference
- Heteroskedasticity
- Asymptotic properties of the OLS
- Quadratics and Interactions
- Functional Form Misspecification
- MLRM with qualitative information
Cursos
Cursos onde a unidade curricular é leccionada: