The main aim is to endow students with basic statistical tools for collecting and analysing univariate and bivariate data for political, economic and social sciences applications. 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. Practical lessons dealing with real-world examples are designed to allow students to improve abilities in collecting, analysing, interpreting and presenting findings and data also using statistical software and advanced spreadsheet (EXCEL). The EU digital competences DIGCOMP 2.1 are developed (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, central tendency and dispersion measures), Probability theory, Statistical inference, association in two way tables, linear regression. Applying knowledge and understanding: ability to select appropriate data analysis methods analyze the relationship between variables in economics, finance and business Making judgments: ability to collect, use and critically interpret quantitative and qualitative data relating to economics and social sciences, achieved through the analysis of documents issued by official national and international statistics, scientific articles on statistical methods and applications, case studies. Digital competences are developed. Communication skills: effective communication skills of data analysis works - achieved through written tests and the presentation of research results on empirical data. Learning skills: ability to learn autonomously data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of statistical methods applied in economics and social sciences

Theoretical lessons: Statistical variables. Frequency distributions. Data Graphical representations. Measures of position. Variability. Random experiment and events. Probability axioms and theorems. Conditional probability. Independence. Univariate and bivariate random variables, discrete and continue random variables. Probability distributions. Central limit theorem. Introduction to random sampling. Point Estimation. Interval Estimation. Statistical hypothesis testing. Correlation. Practical lessons dealing with real-world examples in social sciences

Cicchitelli, P. D'Urso, M. Minozzo Titolo: "Statistics: Principles and Methods" with MyLab – Pearson Seeing Theory: https://seeing-theory.brown.edu

Lectures, exercises, applied exercises, interactive visualization, case studies in social sciences based on real data, also using statistical and econometric packages and advanced spreadsheet

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 a maximum of three students 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 solutions to solve problems. The written test consists of three partial tests with an evaluation out of ten, the last one to be held in the first appeal of exams. In the appeals of the first session it is possible to keep valid the first two partial tests and take only the third test (however, only once - the withdrawal is not counted). In the case of three tests, the final score is the sum of the scores in the three tests. If the student is not satisfied with the grade obtained, he or she can renounce and take the entire exam. The Project Work involves an additional score of up to 1 point on the grade obtained in the written test. 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 or 1 point), 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 (within one week from the end of the written examination). 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 essay is a work in which statistical methods are applied in politics, economics or society. The topic is agreed with the teacher.