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 (EXCEL, R, R Markdown – Literate Coding). 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 (EXCEL, R, R Markdown – Literate Coding).

A.C. MONTI (2008), Introduzione alla Statistica, ESI. 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 is the in the form of a written test consisting of both theoretical and empirical questions, including multiple choice questions, and a project work on real data. It verifies the acquisition of Knowledge and understanding, Applying knowledge and understanding, Making judgements. 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 exam can be alternatively done by three partial tests. 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. WRITTEN EXAMINATION: this type of examination ("scritto verbalizzante") consists in a written test without a subsequent oral examination. The student must book for the written test. At the end of the final examination, the teacher corrects the homework and publishes the results on the dedicated VOL web page. The students enrolled in the final exam will receive a communication with the results of the final examination (the outcomes of the written examination will also be displayed on the web self service). Since the publication of the results, the student has 3 days to reject the grade. Once the 3-day period is elapsed, the rule of "tacit consent" applies and the examination result is verbalized by the teacher. The teacher has to close down the verbal through the digital signature. Once the verbal is closed down, the student receives an e-mail communication reporting the mark obtained. The text of the final proof and the corresponding solutions are made available on the class website before the publication of the results. Each candidate can access the solution of the written exam in a way that, independently from the final outcome of the exam, the student will be on time so to be able to reject the proposed vote.

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