The course intends to train towards a understanding of applications of probability theory for two important statistical techniques including their specific applications.
The course introduces the student to the exploratory statistical analysis of multivariate data by pointing out the mathematical aspects and by developing the essential skills for the interpretation of the data under investigation. Laboratory sessions provide students with the opportunity to analyse, discuss, and solve real problems.
At the end of the course students will be able to
At the end of the course students will
By participating in the planned group activities, at the end of the course the student will have acquired the following basic skills
Probability and basics of statistical inference.
Combination of traditional lectures and lab sessions with the software R.
Group activities to develop: basic literacy skills, project creation skills, the ability to learn how to learn, and advanced social skills.
Statistical sampling from a finite population. Sampling schemes, simple random sampling with and without replacement. Stratified sampling. Proportional allocation and optimal allocation. Statistical estimators of means and their variances.
Time series: exploratory analysis. The notions of stationarity and ergodicity. Strong and weak stationary processes. Autocovariance function and partial autocovariance function. SARIMA models.
Sampling theory 1. Vic Barnett Sample Survey, Principle and methods, Third Edition, John Wiley & Sons, Ltd, 2002 2. William Cochran, Sampling Techniques, John Wiley & Sons, 1977 3. Sharon L. Lohr, Sampling: Design and Analysis. Second Edition, Brooks/Cole, 2010 4. Pier Luigi Conti, Daniela Marella: Campionamento da popolazioni finite, Springer-Verlag Italia 2012 5. Handouts provided by the lecturer Time series 1.C. Chatfield (1980). The analysis of Time Series: an introduction, Chapman and Hall 2. Rob J Hyndman and George Athanasopoulos (2nd edition). Forecasting: Principles and Practice, Monash University, Australia https://otexts.com/fpp2/ 3. R.D. Pend e F. Dominici (2008). Statistical methods for environmental epidemiology with R. A case study in air pollution and Health, Wiley 4. R.H. Shumway e D.S. Stoer (2000). Time series analysis and its applications with examples in R, Springer
Ricevimento: For organizational issues contact by email Eva Riccomagno <eva.riccomagno@unige.it>
September 22, 2025
Written exam with multiple choice and open questions. Two group projects on topics agreed with the teachers. Discussion of the reports and written test.
Upon request by the student the exam can be held in English.
Main points of evaluation are the level of acquisition of the learning objectives and the ability to communicate in a written report the data analyses carried out during the course.
Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison.
