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)

The course aims to review and deepen the fundamental concepts of descriptive statistics, sampling, statistical inference, and regression models applied to business contexts. The objective of the course is to provide the appropriate tools to address problems related to quantitative and statistical information, and to properly interpret the results of statistical and quantitative analyses. Alongside the mathematical and statistical aspects, the course places significant emphasis on developing conceptual and logical aspects, as well as on application to real-world cases, also with the aid of software. The course consists of delivery-based teaching (DE) (approx. 5 ECTS), and interactive teaching (DI) (approx. 3 ECTS), which includes practical exercises and group work.

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.)

Lectures including both theory and exercises with dedicated softwares. Interactive practice sessions and computer lab activities.

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: a discussion of a application to a real-world business and economic case using dedicated software, aimed at assessing the practical skills acquired; and an oral exam aimed at verifying theoretical knowledge.

Please note that after the third exam attempt within a calendar year, registration is automatically blocked until the following calendar year.

In particular, the oral exam includes:

theoretical questions to test knowledge of statistical methods and command of terminology;

problems based on the understanding of business and financial data and on commenting on the results, aimed at evaluating the capacity for independent statistical analysis.

Please note that after the third exam attempt within a calendar year, registration is automatically blocked.
In addition to the official exams, a midterm exam (prova parziale) is provided according to the procedures described in detail on the course's DIR platform.

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..

Descriptive Statistics: A review of analysis techniques for qualitative and quantitative statistical variables in practical applications.Sampling: Simple random sampling and sampling distributions. Sampling from finite populations.Statistical Inference: Confidence intervals and hypothesis testing.Regression Models: Simple and multiple linear regression. Logistic regression.Time Series Analysis.The syllabus may be subject to minor changes.

By the end of the course, students will have acquired the skills and critical thinking abilities necessary to conduct data analyses, model relationships between variables, and interpret the results.