Class Statistical Methods

This course provides students with the tools of advanced econometric methods that are useful to them in intervention areas, the relevance of the context of the Master in which they fit, and in their professional life. In this context, is expected that students gain a good working knowledge of the most commonly used statistical methods.
Thus, students will learn how to collect, analyze, and draw conclusions from data using statistical tools and techniques and training in statistical computing, allowing them to expand their understanding of computing as applied to statistical problems. Students will also learn how to build multivariate statistical models, evaluate their performances, and interpret the results.

1. Review of Statistics Concepts, Data Analysis - some methodologies.

2. Introduction to Software (R and SPSS) to use in the discipline and data analysis using them

3. Multivariate Analysis of Variance (MANOVA)

4. Multiple Linear Regression

5. Discriminant Analysis

6. Principal Components Analysis

7. Cluster Analysis

The innovative methodology chosen to apply is Project-Based Learning which involves students working on a project that requires them to apply statistical methods to a real-world problem. Students work alone or in groups (preferably) to collect and analyze data and present their findings to the class.

Wickham, H. e Grolemund, G, R for Data Science, Editor: O'Reilly, ISBN 9781491910399

Piairo, H. e Pereira, M., Introdução à Estatística - Em R e SPSS, Editor: Chiado Books, ISBN 9789896978778

Reis, E., Estatística Multivariada Aplicada, Editor: Edições Sílabo, ISBN 9789726182474