STATISTICS (Canale B)

Description

Objectives: The student must be able to understand and use the main tools of descriptive and inferential statistics in the analysis of experimental or observational data. Assessment: The student will be assessed through a written test, including exercises and theoretical questions regarding the entire program. There is also an intermediate test that will allow students who are assessed positively to be exempted from part of the final test. Teaching Methods: The lectures will be supported by slides of the teacher. Weekly exercises are planned to prepare for the final exam. Prerequisites: The student must be able to easily handle general mathematical tools, such as function study and differential and integral calculus. Contents: The course is mainly divided into three parts. The first part will deal with descriptive statistics topics, such as frequency tables, position indices and variability indices. The second part will formally introduce the calculus of probabilities and illustrate the main properties of random variables. The third part will present the theory of statistical inference, and will deal with topics such as point and interval estimation, hypothesis testing. Reference Texts: Cicchitelli, P. D'Urso, M. Minozzo, "Statistics: principles and methods" Thesis assignment criteria: - Extended Program And Reference Reading Material: Week 1: - Extended Program And Reference Reading Material: Week 10: - Extended Program And Reference Reading Material: Week 11: - Extended Program And Reference Reading Material: Week 12: - Extended Program And Reference Reading Material: Week 2: - Extended Program And Reference Reading Material: Week 3: - Extended Program And Reference Reading Material: Week 4: - Extended Program And Reference Reading Material: Week 5: - Extended Program And Reference Reading Material: Week 6: - Extended Program And Reference Reading Material: Week 7: - Extended Program And Reference Reading Material: Week 8: - Extended Program And Reference Reading Material: Week 9: - Intended learning outcomes: The student will be able to synthesize experimental or observational data through synthetic measures; to apply probability calculus to real problems and to correctly use inferential theory to construct quantities of interest