CODE 108582 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 2 STATISTICA MATEM. E TRATTAM. INFORMATICO DEI DATI 8766 (L-35) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/06 LANGUAGE Italian (English on demand) TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW The course intends to train towards a understanding of applications of probability theory for two important statistical techniques including their specific applications. AIMS AND CONTENT LEARNING OUTCOMES 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. AIMS AND LEARNING OUTCOMES At the end of the course students will be able to judge the validity of a sample survey plan and analyze simple sample surveys also by aid of software evaluate mathematical properties of a probabilistic sample develop further knowledge about the theory and practice of statistical sampling present a report with the analysis of a sample and a critique of its design At the end of the course students will be able to perform the analysis of simple time series in the time domain also with software be able to develop further theoretical and computational knowledge for statistical analysis of time series be able to present a simple report about the statistical analysis of a time series possess the essential mathematical and statistical knowledge related to time series By participating in the planned group activities, at the end of the course the student will have acquired the following basic skills alphabetical-functional (group writing up and individual precentation of group work) proficiency in project creation (choice of the project, data sets and statistical/mathematical techniques, choice of both written and oral presentation styles) ability to learn to learn (group learing of topics essential for the analysis) social competence, advanced level (subsumed by the amount and types of choices to be made by the group) PREREQUISITES Probability and basics of statistical inference. TEACHING METHODS 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. SYLLABUS/CONTENT 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. RECOMMENDED READING/BIBLIOGRAPHY 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 TEACHERS AND EXAM BOARD FRANCESCO PORRO EVA RICCOMAGNO Ricevimento: For organizational issues contact by email Eva Riccomagno <eva.riccomagno@unige.it> LESSONS LESSONS START According to the academic calendar. Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION 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. ASSESSMENT METHODS 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. FURTHER INFORMATION 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. Agenda 2030 - Sustainable Development Goals No poverty Quality education Gender equality Decent work and economic growth OpenBadge PRO3 - Soft skills - Sociale base 1 - A PRO3 - Soft skills - Imparare a imparare base 1 - A PRO3 - Soft skills - Creazione progettuale base 1 - A PRO3 - Soft skills - Alfabetica base 1 - A