Title: Statistical methods
Instructors: Corrado Lagazio, corrado.lagazio@unige.it
Marta Nai Ruscone, marta.nairuscone@unige.it
Fabio Rapallo, fabio.rapallo@unige.it
Credit (CFU): 3
Lectures: 24 hours
Reading and Essay Writing: 45 hours
Period Taught: January-February
Course Description and objectives
This course aims at presenting a set of statistical models and methods for advanced data analysis. At the end of the course, students will know the main multivariate and nonparametric analysis tools with applications in Economics and Social Sciences. Student will be able to analyze multivariate data sets, to communicate the results and their implication, and to write a report with methods, results, and discussion. The following is a tentative list of topics: distance-based and model-based clustering; principal component analysis and inference; exploratory factor analysis; semi- and non-parametric regression; regularization techniques. The detailed list of topics will be defined based on the actual statistical and computational skills of the students.
Prerequisites
It is expected that students have prior knowledge of Statistics, in particular: Descriptive statistics; Probability; Inference; Linear Regression.
Course Materials
Lecture slides and R scripts.
Textbooks: 1) Hastie, Tibshirani, Friedman. The Elements of Statistical Learning, 2nd edition, Springer, 2008. 2) Everitt, Dunn. Applied Multivariate Data Analysis, Wiley, 2001.
Assessment
Students will be assessed based on their performance in the final exam, which will consist of the public presentation and discussion of a written report.