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Academic Year 2023/2024 - Teacher: Vittorio ROMANO

Expected Learning Outcomes

The aim of the course is to give the main tools for statistical investigations along with the study of advanced subjects for tackling problems of interest  in mathematical physics, economics, industry and in general for applications arising from applied sciences. In particular, the course  furnishes the background for  mathematical analysis in economics and therefore suitable for student in the program of finance.  

Some aspects treated in the course are in any case relevant for those who want to teach Mathematics at the high schools.  

In particular, the course aims to allow the student to acquire the following skills:

knowledge and understanding:  knowledge of results and fundamental methods  in statistics. Skill of understanding problems and to extract the major features. Skill of reading, understanding and analyzing a subject in the related literature and present it in a clear and accurate way. 

applying knowledge and understanding: skill of elaborating new example or solving novel theoretical exsercise,  looking for the most appopriate methods and applying them in an appropriate way. 

making judgements: To be able of devise proposals suited to correctly interprete complex problems in statistics and its applications.  To be able to formulate autonomously  adequate judgements on the applicablity of numerical methods or statistical models to theoretical or real situations. 

communication skills: skills of presenting arguments, problems, ideas and solutions in mathematical terms with clarity and accuracy and with procedures  suited for the audience, both in an oral and a written form. Skill of clearly motivating the choice of the strategy, method and contents, along with the employed computational tools.

learning skills: reading and analyzing a subject in the literature involving applied mathematics. To tackle in an autonomuous way the systematic study of arguments not previously treated.  To acquire a degree of autonomy such that the student can be able to start with an autonomuos reserach activity.

Course Structure

Mainly frontal lectures. Moreover, the theoretical acquired competencies will be applied in a laboratory where study cases will be tackled in a MATLAB environment. 

If restrictions will be introduced because the COVID pandemic, le lectures will be given in a mixed way or only online and some changes could be introduced to assure the accomplishiments foreseen for the course. 

Learning assessment may also be carried out on line, should the conditions require it.

Required Prerequisites

Some basic knowledge of calculus of probability is welcome but not  strictly necessary. 

Attendance of Lessons

Strongly suggested

Detailed Course Content

  1. Reminds and complements of probability. Estimators. Characteristic functions. Various forms of large number law. Central limit theorem. Descriptive statistics. Inferetial statistics. Hypothesis tests. Non parametrical estimators. Linear regression and analysis of variance. Elements of programming in MatLab.

Textbook Information

V.  Romano, Metodi Matematici per i Corsi di Ingegneria, CittàStudi

P. Baldi Calcolo delle probabilità e statistica, McGraw-Hill

R. Scozzafava Incertezza e probabilità, Zanichelli

D. C. Montgomery, G. C. Runger Applied statistics and probability for engineers, J. Wiley 

Course Planning

 SubjectsText References
1Complements of probability. Characteristic functions, large number law in several forms. Descriptive  Statistics. Statistical inference. Tests of hypothesis. Linear regression and analysis of variance. Notes of the lecturer
2MATLAB programmingNotes of the lecturer

Learning Assessment

Learning Assessment Procedures

Written report with applications in Matlab and oral exam.

If necessary the exam will be  online.

Examples of frequently asked questions and / or exercises

Characteristic functions. Multivariate normal laws. Law of large numbers. Central limit theorem. Estimators. Confidence intervals. Test on the mean and on the variance. Chi-square test. Cochran's theorem. Linear (simple and multiple) regression. Analysis of variance.