Good knowledge of the fundamentals of Statistics (i.e. descriptive statistics, probability, inferential statistics, simple linear regression model).
This course aim at offering an extensive view of the linear regression model.
As first generalization, the class of generalized linear models will be introduced. Moreover, the issues arising when a large number of covariates is available will be tackled via the penalized approach (LASSO and RIDGE regression).
In the second part of the course a gentle introduction to the Bayesian approach to statistics will be discussed in order to easily introduce multilevel and hierarchical models.
At the end of the course the student will gain the ability to:
a) choose, apply and test appropriate regression model for the analysis of different types data;
b) use the free open-source statistical software R (http://www.r-project.org) for the statistical analysis; as well as of the software Jags (https://mcmc-jags.sourceforge.io/)
c) interpret the results in a decision making perspective.
• Multiple regression
• Penalized regression (Lasso and Ridge regression)
• Generalized Linear models
• (Gentle) Introduction to Bayesian statistics and MCMC algorithms
• Bayesian multilevel models (advanced)
• Software: R, Jags for Bayesian analysis
The course consists in class lectures and R/Jags lab sessions. The lectures & labs calendar will be published at the beginning of the course on the Moodle e-learning platform. Labs will take place within the hours scheduled for the course (2 hours per week, approximately).
The exam consists in:
- a test including open-ended and T/F questions (concerning theoretical topics or short applications of the studied methods);
- exercises to be solved with the use of the R/Jags software (in order to evaluate the ability of the student in analysing different kind of data and in the interpretation of statistical outputs).
The two parts of the exam (theoretical and practical) are each worth 50% of the total score, approximately. A positive evaluation of the theoretical part is required in order to pass the exam.
- Attending class lectures and R labs is strongly recommended.
- If the course will be delivered online (totally or partially), changes may occur in the program and/or in the exam.
- Documentation about R software is freely available at the following link: https://www.r-project.org/other-docs.html.
- Documentation about the Jags software is freely available at the following link: https://mcmc-jags.sourceforge.io/