Statistics for Data Science
Objectives
- Jorge M. Mendes (jmm@novaims.unl.pt); Office/Hours: Office 9, 2nd floor/under appointment
- Bruno Damásio (bdamasio@novaims.unl.pt); Office/Hours: Office 138, Colégio de Campolide /under appointment
General characterization
Code
200178
Credits
7.5
Responsible teacher
Jorge Morais Mendes
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
Statistics and linear algebra (recommended)
Bibliography
- 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
Teaching method
The course is based on theoretical-practical and practical classes. Practical classes and problem-solving oriented.
Evaluation method
- (50%) Final exam (1st and 2nd rounds)
- (50%) Project (opcional)
- Final grade: máximum (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.
Subject matter
- 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