Ateneo - UPO

The course illustrates some of the most important statistical methods for analyzing economic-business data related to business management.
The content is mainly modulated in the following areas. Descriptive statistics; Sampling and statistical inference; simple and multiple linear regression models and logistic regression model; time series analysis. All topics covered will be contextualized to the economic and business field.

Newbold, Carlson, Thorne. Statistica 9/ed. Pearson (2021)
Matilde Bini - Graziano Scaffai - Vieri Panta. Excel per le decisioni aziendali. Pearson (2025)
Francesca Ieva, Chiara Masci, Anna Maria Paganoni. Laboratorio di Statistica con R. 2/Ed. Pearson (2016)

Additional reference texts:
L. Biggeri, M. Bini, A. Coli, L. Grassini, M. Maltagliati. Statistica per le decisioni aziendali. Pearson (2017)
D.C. Montgomery. Controllo Statistico della Qualità. Seconda Edizione. McGraw hill (2006)

Further teaching material prepared by the professor will be published on the web page of D.I.R. (https://www.dir.uniupo.it)

An equivalent knowledge to that acquired in the following courses is required: Mathematical Methods I and II and Statistics of the bachelor degree CLEA. (Detailed programs and Lecture Notes can be found on their specific D.I.R. web pages; an equivalent textbook is: Fulvia Mecatti, Statistica di base, 2/ed., McGraw-Hill.)

Further information will be published during the course on D.I.R. (https://www.dir.uniupo.it).
Le studentesse e gli studenti con disabilità o con Disturbi Specifici dell’Apprendimento (DSA) o con Bisogni Educativi Speciali (BES) possono richiedere servizi e strumenti specifici a loro dedicati rivolgendosi allo Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti e consultando la pagina dedicata del sito di Ateneo: https://uniupo.it/it/servizi/servizi-studentidisabili-e-dsa Le studentesse e gli studenti con disabilità, DSA, BES, una volta preso contatto con lo Staff di Ateneo, possono contattare la/il docente titolare dell'insegnamento in relazione alla declinazione delle modalità di esame.

The exam is oral and consists of:

- The presentation and discussion of a real-world business/economic case study developed using the dedicated software, aimed at testing the practical skills acquired;
- Three questions designed to assess the theoretical competencies acquired. Specifically, these will include:
Questions on the course syllabus (as detailed in the class register on the DIR platform) to verify knowledge of statistical methods and mastery of technical terminology;
Problem-solving tasks based on the interpretation of economic and financial data and the discussion of results, to evaluate independent statistical analysis capabilities.

Further details regarding the exam will be published on the course's DIR page.

Registration for the exam via the student portal is mandatory. Once the maximum number of attempts allowed by the current University Teaching Regulations has been reached, the system will not permit any further registrations.

Grade ranges correspond to the following descriptors:

Grade Range < 18 (Fail / Insufficient)
Severely deficient knowledge of the principles of descriptive and inferential statistics; inability to apply models in a business and economic context or to use dedicated software; confusing or completely inadequate presentation.

Grade Range 18–24 (Satisfactory / Fair)
Basic but superficial knowledge of descriptive statistics and inference theory, with gaps in specific topics; application to real business and economic datasets; limited ability to interpret results or understand their validity and limits; concise but correct presentation.

Grade Range 25–28 (Good / Very Good)
Solid knowledge of the principles of descriptive statistics and inference theory; independence in applying models to real business and economic datasets; critical thinking; rigor and appropriate technical language.

Grade Range 29–30 cum Laude (Excellent)
Complete, fluid, and in-depth mastery of descriptive statistics and inference theory; high critical ability; independence and originality in applying models to real business and economic datasets; rigor and appropriate technical language, demonstrating a comprehensive overview.

Knowledge and understanding
By the end of the course, students will have acquired the main methods of descriptive and inferential statistics. They will also be able to understand and analyze quantitative and decision-making problems under uncertainty, relevant to the business context.

Applying knowledge and understanding
Students will have mastered these tools and will be able to perform data analysis and build variable relationship models within a business and professional context using appropriate software, as well as interpret the results.

Making judgements
Students will be able to understand and critically compare quantitative-statistical and probabilistic analyses that may arise in professional practice.

Communication skills
Students will be able to describe statistical and probabilistic analyses and clearly and correctly communicate their meaning.

Learning skills
Students will be able to use the acquired tools to tackle problems similar to those covered during the course and to further explore subsequent quantitative-statistical topics in a professional context.

Minimum expected level of learning: To achieve a passing grade, students must demonstrate a knowledge of the fundamental concepts of the statistical and probabilistic methods covered in the course and the ability to apply them correctly to standard problems, including with dedicated software.

Advanced expected level of learning: An advanced level is achieved when, in addition to a full knowledge of the tools and techniques covered, students demonstrate the ability to independently address non-standard problems, justify their choices, and critically interpret the results.