37993 - Business Statistic

Learning outcomes

The course covers statistical methodologies useful for the collection, organisation and analysis of business data. The results of these analyses can be helpful for managerial decisions, often made under conditions of uncertainty. The course is application-oriented, with the use of STATA and R statistical softwares, of the mentioned methods to business data, in the context of specific issues/scenarios of the business management.

Course contents

The course explores the main statistical methods used in business and economic analysis, with particular reference to accounting, corporate finance and business analytics.

The course is application-oriented and focuses on the use of the statistical software Stata and R to apply the techniques discussed to business and economic data through case studies in business analysis and management.

1. Business Data and Information Systems

• Statistical sources for business analysis

• Data quality and data cleaning

• Construction of economic and financial indicators

• Statistical analysis of financial ratios

• Analysis of performance distributions

• Statistical benchmarking across firms

Applications

• Analysis of financial ratios using statistical methods

• Analysis of firms operating in different sectors

• Dashboard construction

2. Statistical Models for Business Problem Solving

• Multiple linear regression

• Dummy variables; Variable selection; Model diagnostics; Multicollinearity; Economic interpretation of coefficients

• Time-series, cross-sectional and panel data

• Logarithmic transformations

• Event study methodology

• Models for binary dependent variables

• Logit model

• Credit ratings and probability of default

Applications

• Determinants of profitability

• Event study

• Credit scoring model

3. Business Analytics and Data Mining

• Customer segmentation

• Cluster analysis

• Decision trees

• Introduction to machine learning for business data

• Interpretation of predictive models

Applications

• Customer segmentation

• Churn analysis

 

Prerequisites. Random variables, estimators and their properties, confidence intervals, hypothesis testing, and the simple linear regression model.

Readings/Bibliography

Teaching materials available on the Virtuale e-learning platform.

 Reference textbooks:

F. Bassi and S. Ingrassia, Statistica per analisi di mercato. Metodi e strumenti, Pearson, Milan, 2022.

S. Brasini, M. Freo, F. Tassinari and G. Tassinari, Marketing e pubblicità. Metodi di analisi statistica, Il Mulino, Bologna, 2010.

E. Supino, Modelli predittivi per la diagnosi della crisi d’impresa, Wolters Kluwer CEDAM, Milan, 2025

Teaching methods

The course consists of a combination of theoretical lectures, applied case studies and quantitative analysis.

Students will use Excel, Stata and R.

Practical sessions will be based on data from official databases (ISTAT, Eurostat, Banca d’Italia, AIDA/Orbis, where available) and from companies’ financial statements.

Given the type of activities and teaching methods adopted, attendance of this course requires all students to have previously completed Modules 1 and 2 of the mandatory training on health and safety in the workplace, delivered online. The modules are available at:

https://corsi.unibo.it/laurea/EconomiaAziendale/formazione-obbligatoria-su-sicurezza-e-salute

Assessment methods

Students will be required to conduct a statistical analysis and present their findings orally. They may choose a topic and dataset that they find interesting and relevant. The topic and dataset must be agreed upon with the instructor before starting the analysis, in order to ensure that the proposed analysis and dataset are appropriate.

The project must be completed independently by groups of one to three students. Presentations will be held on the official exam date.

The final grade will be based on the accuracy and completeness of the analysis, the correctness and relevance of the terminology used, and the clarity with which the results are presented.

Possibility to reuse the grade: 1.

Teaching tools

Additional teaching materials (such as scientific papers, slides, datasets, etc.) will be provided during the lessons and will be accessible through the Virtuale e-learning platform.

Office hours

See the website of Marzia Freo