The main aim is to endow students with basic statistical tools for collecting and analyzing 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 that are subject to uncertainty. Statistical inference provides methods for analyzing data obtained from random experiments. Practical lessons dealing with real-world examples are designed to allow students to improve their abilities in collecting, analyzing, interpreting, and presenting findings and data, also using statistical software and advanced spreadsheet (EXCEL). The EU digital competencies 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, and linear regression. Applying knowledge and understanding: ability to select appropriate data analysis methods to 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, and case studies. Digital competencies are developed. Communication skills: effective skills in communicating data analysis output 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. Descriptive statistics: Basic concepts. Statistical variables. Frequency distributions. Data graphical representations. Measures of central tendency. Index numbers. Variability. Joint distributions: dependence, regression, correlation. Probability. Random experiments and events. Probability axioms and theorems. Discrete and continuous random variables. Probability distributions. Statistical inference: Sampling distributions. Point Estimation. Interval Estimation. Statistical hypothesis testing. Practical lessons dealing with real-world examples in political, economic, and social sciences.

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

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

The final examination is in the form of a written test and a Project Work on real data. The written test (scritto verbalizzante) is a type of examination that does not include a subsequent oral examination. It consists of both theoretical and empirical questions, including multiple-choice questions. It verifies the acquisition of Knowledge and understanding, Applying knowledge and understanding, Making judgments. The written test is divided into two tests. The first test is taken during the midterm week (from March 8 to March 13, 2027); the second test can be taken during the Summer Exam Session or the Autumn Exam Session (note that the student can take it just once; if she/he withdraws, the test is not counted). Both the first test and the second test are scored out of fifteen. The final score is the sum of the scores in the two tests. If a student does not pass the first test, she/he will take the complete test with an evaluation out of thirty. If the student is not satisfied with the grade obtained in the midterm test, she/he can refuse it and take the complete test. The Project Work on real data consists of a 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, and development of new strategies and solutions to solve problems. The Project Work involves an additional score (1 point) to the grade obtained in the written test. Therefore, 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 during the first exam session. At the end of the final examination, the teacher 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 communication with the results of the examination (the outcomes of the written examination will also be displayed on the web self-service). The student has no option to refuse the grade. She/he will be allowed to withdraw for the duration of the test, but in case the student turns in the assignment, the grade received may not be refused.

The final essay is a work in which statistical methods are applied in politics, economics, or society. The topic is agreed upon with the teacher.