Unit STATISTICAL METHODS FOR RISK MANAGEMENT
- Course
- Accounting, finance and control
- Study-unit Code
- A004840
- Curriculum
- Accounting & management strategy
- Teacher
- Francesco Bartolucci
- Teachers
-
- Francesco Bartolucci
- Elena Stanghellini (Codocenza)
- Hours
- 21 ore - Francesco Bartolucci
- 21 ore (Codocenza) - Elena Stanghellini
- CFU
- 6
- Course Regulation
- Coorte 2025
- Offered
- 2025/26
- Learning activities
- Caratterizzante
- Area
- Discipline statistiche e matematiche
- Sector
- SECS-S/01
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- Inferential statistics tools applied to the business context. The simple and multiple regression model. Categorical data analysis. The simple and multiple logistic regression model. Credit Scoring as a tool for evaluating a company.
- Reference texts
- G. Cicchitelli, P. D’Urso e M. Minozzo, Statistica - Principi e metodi (quarta edizione), Pearson, Milano, 2022.
Lecture notes from the book: E. Stanghellini, Introduzione ai metodi statistici per il credit scoring, Springer, 2009. - Educational objectives
- Modern business management requires familiarity with advanced quantitative analysis tools for micro- and macro-economic variables. Students will be able to complete multidimensional business data analyses and correctly interpret the relevant statistical measures.
- Prerequisites
- To successfully complete the module, students must have basic knowledge of descriptive and inferential statistics.
- Teaching methods
- Classes are held for four hours per week. Significant time will be devoted to the analysis of business data using the statistical software R.
- Other information
- Erasmus students or other international exchange students are most welcome.
- Learning verification modality
- Assessment of learning will be carried out through a written exam consisting of four questions, two theoretical and two practical. The theoretical questions will focus on statistical tools, while the practical questions will consist of interpreting the typical output of statistical data analysis software.
- Extended program
- Review of statistical inference: point estimation; interval estimation; hypothesis testing.
The simple regression model: basic assumptions; least squares method; coefficient of determination; maximum likelihood estimation of parameters; confidence intervals; hypothesis testing on parameters; examples of applications in R to business cases.
The multiple regression model: basic assumptions; formulation of the model using matrix notation; least squares method; multiple coefficient of determination; maximum likelihood estimation of parameters; confidence intervals; hypothesis testing on parameters; introductory model diagnostics; examples of applications in R to business cases.
Categorical data analysis tools: hypothesis testing and confidence intervals for proportions in two or more populations; examples of applications in R to business cases.
The logistic regression model: basic assumptions; model formulation and interpretation of parameters; maximum likelihood estimation (overview); confidence intervals and hypothesis testing on parameters; examples of applications in R to business cases.
Credit Scoring: introductory concepts and main analysis methods. - Obiettivi Agenda 2030 per lo sviluppo sostenibile
- The module contributes to the achievement of Goal No. 4, as it provides tools for data analysis in the field of management and business administration, which are particularly important in the era of Big Data.