The main aim is to endow students with basic statistical tools for collecting and analysing univariate and bivariate data. Descriptive statistics provide methods for data explorative analysis. Probability theory provides models for phenomena which are subject to uncertainty. Statistical inference provides methods for analysing data obtained from random experiments. At the end of the course, the students will be able to apply basic statistical tools to analyze real-world economic data, also by means of statistical software and advanced spreadsheet and scraping software (EXCEL, R, R Markdown – Literate Coding, Apify). The course also develops digital competences as of EU DIGCOMP 2.1 (Competence area 1: Information and data literacy; Competence area 2: Communication and collaboration; Competence area 3: Digital content creation).

Knowledge and understanding: knowledge of data types and related univariate analysis techniques (frequency distributions, graphical representations, measures of central tendency and measures of dispersion), probability theory and statistical inference, dependency analysis in double tables, linear regression model. Applied knowledge and understanding: ability to select appropriate measures of data synthesis and analysis of the relationship between variables in economics, finance and business. Making judgments: ability to collect, process and critically interpret quantitative and qualitative data related to economic, financial and business phenomena. Communication skills: ability to effectively communicate data analysis. Learning skills: ability to learn autonomously data analysis techniques, in professional activities or subsequent studies.

Theoretical lectures. Statistical variables. Frequency distributions. Data Graphical representations. Measures of location. Variability. Random experiment and events. Probability axioms and basic theorems/properties. Conditional probability. Independence. Univariate and bivariate random variables, discrete and continue random variables. Common probability distributions. Central limit theorem. Introduction to random sampling. Point Estimation. Interval Estimation. Statistical hypothesis testing. Correlation. Simple linear regression. Practices. Practices are designed to develop the students’ ability in collecting and analyzing data in economics, finance or management, and in analyzing them also by means of statistical software and advanced spreadsheet and scraping software (EXCEL, R, R Markdown – Literate Coding, Apify).

A.C. MONTI (2023), Statistica: Esercizi Svolti – Pearson ISBN: 9788891927408 con piattaforma Mylab Seeing Theory https://seeing-theory.brown.edu/

Lectures Interactive visualization Exercises Empirical exercises with EXCEL, R, R Markdown (Literate Coding). Flipped Classroom Case and research analysis Data from official statistical sources

L'esame finale è costituito da una prova scritta e da un Project Work su dati reali. L'esame scritto consiste in due prove con valutazione su trenta. La prima prova si svolge durante la settimana intermedia (dal 20 al 26 ottobre 2025); la seconda prova si svolge durante la prima sessione d’esame. Il punteggio finale è la somma dei punteggi delle due prove. La prima prova e la seconda prova hanno una valutazione in quindicesimi. Durante la prima sessione è possibile mantenere valida la prima prova e sostenere solo la seconda (si noti che lo studente può sostenerla una sola volta; in caso di ritiro, la prova non viene conteggiata). Se lo studente non supera la prova intermedia, può sostenere la prova completa con una valutazione su trenta. La valutazione della prova intermedia può non essere accettata; in tal caso lo studente può svolgere la prova completa durante la sessione d’esame. Per le prove svolte durante la sessione d’esame è consentito allo studente di ritirarsi per tutta la durata della prova, mentre in caso di consegna del compito, il voto ottenuto non potrà essere in alcun modo rifiutato dallo studente. La prova scritta consiste in domande teoriche ed empiriche, anche a scelta multipla. Verifica l'acquisizione di Conoscenza e comprensione, Applicazione di conoscenza e comprensione, Autonomia di giudizio (Descrittori di Dublino 1, 2, 3). The Project Work involves an additional score of up to 2 point on the grade obtained in the written test during the first exams session. The final grade of the exam is obtained by adding to the outcome of the written test the grade obtained in the Project Work (0, 1, 2 points), limited to the test taken - with any outcome - in one of the sessions of the first exam session.

The final dissertation concerns applications of statistical methods in business and economics. The topic is agreed with the lecturer.