Ateneo - UPO

The course illustrates some statistical methods that are of interest for the training of a lawyer. Topics are: descriptive statistics, probability calculus and decision problems linked to sample statistics.

Mecatti, F. Statistica di Base. Come, quando e perché. 3rd ed. (2022). Milan: McGraw-Hill.Additional teaching materials will be made available on the course's DIR page.

The main objective of the course is to understand numerical/statistical and probabilistic analyses, as well as the inherent potential of the tools presented as legal expertise.

The course is worth 3 ECTS credits, corresponding to 22.5 hours of instruction. Overall, it includes approximately 15 hours of direct teaching (DE) and 7.5 hours of interactive teaching (DI). The breakdown between the two modalities is flexible and may vary across individual lessons.

Basic algebra.

Lectures and practical exercises.

Attendance is not mandatory but is strongly recommended. Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities. Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The exam consists of a compulsory written test, with a possible confirmation oral exam, and an optional oral exam.

The written test consists of 8 multiple-choice questions (4 related to Quantitative Methods 1 and 4 related to Quantitative Methods 2). Each question has 5 possible answers, of which only one is correct, and the test must be completed within 90 minutes. Each answer provided must be accompanied by an explanation/justification. Each correct answer that is properly justified is worth one point. Each incorrect answer, unanswered question, or answer provided without justification or with an incorrect justification receives 0 points.

The written test has a maximum score of 26/30 (8 correct answers) and is considered passed only if the student obtains at least two correct answers in both Quantitative Methods 1 and Quantitative Methods 2. The written test is concluded only after the possible confirmation oral exam, if the Professor deems that clarification on the submitted work is necessary. Failure to attend the confirmation oral exam results in an insufficient grade for the written test. The questions cover the course topics and aim to verify the skills achieved in applying acquired knowledge to solve problems.

The optional oral exam is available to students who obtain a passing grade (greater than or equal to 20/30) in the written test and may result in a change of the written-test score ranging from −3 to +4 points. Students must first demonstrate the ability to identify any errors made in the written exam, pinpointing the correct approaches and providing a clear explanation. In addition, a score of up to 2 points requires demonstrating a sufficiently broad knowledge and understanding of the course contents that, in particular, enables the student to establish connections between different topics. To achieve a score of 3 to 4 points, students must be able to independently apply their knowledge and understanding of the course content to engage in an in-depth discussion on critical aspects related to the subjects covered, using clear and rigorous language.

The final grade is the sum of the grades obtained in the written test and, if taken, the oral exam, up to a maximum of 30/30 with honors. The awarding of honors is subject to the student’s ability to independently solve some questions (exercises or more theoretical questions) of above-average difficulty.

Further details regarding the exam procedures are published on the course DIR page

Population, sample, variable, modality. Frequency distributions and their representations. Summary indicators: averages and measures of variability. Double entry tables and conditional distributions. Linear regression function: calculation of coefficients and measurement of the goodness of fit. Elements of probability calculus. Conditional probabilities and Bayes formula. Samples and estimates (point and interval). Testing of statistical hypotheses: I and II type errors, level and power. Decision rules and p-value.

Students are expected to acquire the fundamental concepts of descriptive and inferential statistics, develop the ability to correctly interpret results, and identify the contexts in which the presented techniques can be effectively applied.