Statistics
Persone
Descrizione
Students are introduced to statistical inference: population and samples, exponential family, likelihood function, point, and interval estimation, introduction to hypothesis testing. The multivariate Gaussian distribution and linear regression are described.
The course provides skills in using the semantics of the free software environment R and / or Python for descriptive univariate and multivariate data analysis, inferential methods, and estimation of statistical models.
Theory and practical applications are jointly developed to support students with deep theoretical and practical knowledge.
Obiettivi
The course aims to provide students with methodological and applied background on selected topics in inferential statistics and linear regression models.
Prior knowledge of the following topics is required: probability theory, expectation and variance of a random variable, main discrete (Bernoulli, Binomial, Discrete uniform) and continuous (Gaussian, Student-T, Gamma) distributions of random variables, and basic notions of matrix algebra.
Knowledge of Python is expected with the simultaneous Programming in Finance and Economics I course.
Obiettivi di sviluppo sostenibile
- Partnership per gli obiettivi
Modalità di insegnamento
In presenza
Impostazione pedagogico-didattica
Lectures ex-cathedra.
Students are requested to bring their laptops when available.
Teaching notes will be distributed during the course.
Modalità d’esame
A final written exam on the theoretical part and an application to develop using the R / Python software.
Bibliografia
- Casella, George, Berger, Roger L.. Statistical inference. 2nd ed.. Pacific Grove, CA: Duxbury Thomson Learning, 2002.
- Core Team, R. R: A Language and Environmental for Statistical Computing.: R. Foundation for Statistical Computing. Vienna (Austria): --, 2022. (https://www.R-project.org)
- Gentle, James E.. Statistical analysis of financial data: with examples in R. Boca Raton: CRC Press Taylor & Francis Group, 2020.
- Sheppard, Kevin. Introduction to Python for Econometrics, Statistics and Data Analysis. 4th edition. Oxford: University of Oxford, 2020. (https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2020.pdf)
Offerta formativa
- Master of Science in Economics in Finance, Lezione, 1° anno