Statistics

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.

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.

• Partnerships for the goals

In presence

Lectures ex-cathedra.
Students are requested to bring their laptops when available.

Teaching notes will be distributed during the course. 

A final written exam on the theoretical part and an application to develop using the R / Python software. 

• Attend classes, participate with questions / discussions and do the homework assignements, 27.04.24 - 27.04.24 (Optional)