Statistics: Data Analysis and Probability

The course is divided into two parts. The first part is devoted to exploratory data analysis techniques. The second part develops the essential concepts of probability calculus that underlie statistical inference, starting with the univariate case and then moving on to the multivariate case. The outline program is as follows:
1.    Exploratory data analysis: description and exploration of data, role of computer, graphical representations.
2.    Introduction to the concept of probability: definition, elementary rules of computation, conditional probability, Bayes' theorem.
3.    Discrete random variables: probability distribution, expected value and variance, binomial and Poisson distribution.
4.    Continuous random variables: density and distribution function, expected value and variance, uniform distribution, normal distribution, elements of simulation.
5.    Discrete and continuous multivariate variables: joint, marginal and conditional distributions; conditional expected value. Independence and correlation between random variables.

Reference book: "Statistics" by Paul Newbold, William Carlson and Betty Thorne. Publisher: Pearson.

The course aims to introduce students to the fundamental tools of statistics that are the basis of modeling and empirical work with economic data.

In presence

Lectures in person.

Online final exam (in person).