CODE 48384 ACADEMIC YEAR 2024/2025 CREDITS 6 cfu anno 2 MATEMATICA 9011 (LM-40) - GENOVA 8 cfu anno 3 MATEMATICA 8760 (L-35) - GENOVA 6 cfu anno 3 MATEMATICA 8760 (L-35) - GENOVA 8 cfu anno 2 STATISTICA MATEM. E TRATTAM. INFORMATICO DEI DATI 8766 (L-35) - GENOVA 6 cfu anno 1 MATEMATICA 9011 (LM-40) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR SECS-S/01 LANGUAGE Italian (English on demand) TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW Introduction to Statistical Inference AIMS AND CONTENT LEARNING OUTCOMES To provide an introduction to concepts and techniques from statistical inference which are fundamental to provide a probabilistic measure of the error committed when estimation is based on a sample from a large population AIMS AND LEARNING OUTCOMES At the end of the course students will be able: to explain the key points defining exploratory data analysis versus statistical inference based on finite samples to possess the main concepts and techniques for computing point estimates, confidence intervals and performing hypothesis testing and for evaluating them to identify the suitable statistical technique and perform the analysis of simple data sets. The computer laboratory sessions are mandatory for SMID students. Their aim is to practice the application of the theoretical models learnt during classroom lectures on simple real case studies and data sets. During the lab sessions the student will be able to verify his/her level on understanding of the theory and its application. By participating to the work groups, debating solution strategies and their presentation, comparing the learning strategies of the different members of the group also with the feedback from the individual mock exams, at the end of the course the student will have acquired the following skills at a basic level at a basic level alphabetical-functional personal competence ability to learn to learn social competence PREREQUISITES Mathematical Analysis: function of a variable, integral calculus. Algebra: elements of vector and matrix algebra. Probability: elementary probability TEACHING METHODS Combination of traditional lectures (40 hours) and exercises sessions (24 hours) Four guided group exercises to develop basic literacy skills, personal competence, ability to learn to learn, social competence. SYLLABUS/CONTENT Estimation. Populations, samples, sources of uncertainty and point estimators. Properties of point estimators. Delta method. Some point estimators and their probability distributions. Confidence intervals. Hypothesis tests. How to define and use a statistical test (hypotheses, errors of the first and second type, critical region). Parametric tests. Tests of large samples. Comparative tests. Some non-parametric tests and examples of inference by simulation and resampling. Statistics and tests for linear multiple models. Confidence intervals for the parameters, estimated values and residuals, "studentized" residuals, test of hypotheses on single coefficients and on subsets of coefficients. Forecast. RECOMMENDED READING/BIBLIOGRAPHY 1. Casella G., Berger R.L. (2002), Statistical Inference, Pacific Grove, CA: Duxbury 2. Mood A.M., Graybill F.A., Boes D.C. (1991), Introduction to the Theory of Statistics, McGraw-Hill, Inc. 3. Ross S.M. (2003), Probabilità e statistica per l’ingegneria e le scienze, Apogeo, Milano 4. Wasserman L. (2005), All of Statistics, Springer TEACHERS AND EXAM BOARD EVA RICCOMAGNO Ricevimento: For organizational issues contact by email Eva Riccomagno <riccomagno@dima.unige.it> GABRIELE MOSAICO Ricevimento: The teacher is available after the lessons and by appointment, to be asked via Teams or email (or in person) GIULIA BERTAGNOLLI Exam Board EVA RICCOMAGNO (President) GABRIELE MOSAICO SARA SOMMARIVA (President Substitute) LESSONS LESSONS START According to the academic calendar Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION The exam consists of a written and a oral part. During the semester there will be three (not evaluated) mock exams. The lecture after each mock exam will start with a 15-minute closed-book written examination. The first two closed-book examinations are evaluated at most 3 marks and the third one at most 2 marks, for a maximum total of 8 marks. For the students who attempted all of the three closed-book examinations, the final written examination consists of a 2-hour open book examination, which is evaluated at most 23 marks to be added to the marks of the three on-course closed-book examinations. For the students who did not attempt the three closed-book examinations, the final written examination consists of two parts: a 45-minute closed-book examination and a 2-hour open-book examination. The closed-book part is evaluated at most 8 points, the open-book part is evaluated at most 23 points. To be admitted to the oral examination, are required either a total of 5 points from the intermediate exams or a total of 16 points from the two parts of the written exam. The mark obtained from the intermediate exams are valid until February. The mark from the written parts is valid only once. ASSESSMENT METHODS The on-course examination and the closed-book part of the final examination test the comprehension of the theory. The two-hour open-book examination evaluates the acquired ability to apply the theoretical ideas for simple data analysis. Exam schedule Data appello Orario Luogo Degree type Note 20/01/2025 09:00 GENOVA Scritto 13/02/2025 09:00 GENOVA Scritto 30/06/2025 09:00 GENOVA Scritto 21/07/2025 09:00 GENOVA Scritto 12/09/2025 09:00 GENOVA Scritto 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. Upon request by the students, the lectures and/or the exam can be held in English. Agenda 2030 - Sustainable Development Goals No poverty Quality education Gender equality Decent work and economic growth OpenBadge PRO3 - Soft skills - Alfabetica base 1 - A PRO3 - Soft skills - Sociale base 1 - A PRO3 - Soft skills - Imparare a imparare base 1 - A PRO3 - Soft skills - Personale base 1 - A