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

The final examination consists of a written test and a Project Work on real data. The exam includes two partial tests, graded out of thirty. The first test takes place during the midterm week (from March 8 to March 13, 2027); the second test is held during the first exam session. The final score is the sum of the scores from the two tests. Both the first and the second tests are graded out of fifteen. During the first session, it is possible to keep the result of the first test and take only the second one (note that students may take the second test only once; if they withdraw, the test is not counted). If the student does not pass the midterm test, they may take the full test graded out of thirty. The result of the midterm test can be rejected; in that case, the student may take the full test during the exam session. For the tests taken during the exam session, students are allowed to withdraw at any time during the test. However, if the test is submitted, the grade obtained cannot be rejected by the student under any circumstance. The exam consists of theoretical and empirical questions, including multiple-choice questions. It assesses the acquisition of Knowledge and understanding, Application of knowledge and understanding, and Making judgements (Dublin Descriptors 1, 2, 3). Project Work. Report on the statistical analysis of empirical data to be carried out in groups of four students (at most), with the aid of the advanced spreadsheet (EXCEL). It verifies the acquisition of Making judgments, Communication Skills, Learning Skills. Digital skills (1, 2, 3), teamwork, time management, development of new strategies and approaches to solve problems. 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.