95966 - Business Data Analysis

Academic Year 2023/2024

  • Docente: Meri Raggi
  • Credits: 6
  • SSD: SECS-S/03
  • Language: English
  • Moduli: Meri Raggi (Modulo 1) Yari Vecchio (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Food Animal Metabolism and Management in the Circular Economy (cod. 5814)

Learning outcomes

At the end of the course the student is familiar with the main statistical methods necessary for the study of economics. Furthermore, the student is able to use the main statistical techniques and, when possible, apply them to the contents of the sector.

Course contents

Theroretical part:

Statistical variables. Population and sample. Collecting and organizing data. Data set and frequency distributions. Graphic representations.

Mean values, mode, median and quantiles. Eterogeneity and variability and inequality measures.

Shape: asimmetry and curtosis.

Bivariate statistical analysis. Relationships between variables: statistical dependence, covariance and linear correlation. Simple linear regression.

Introduction to sampling and statistical inference. Bayesian theorem. Random variables and probability distributions. Sampling. Point and interval estimation. Hypothesis testing.

Applied part:

During the course the student The course will confront the student with a number of case studies covering different topics and application area. Dataset will be provide to apply theoretical issues


The course is mainly based on lecture notes and chapters/articles provided through the e-learning platform.

An useful background textbook could be: Cicchitelli et al. Statistics, Principles and Methods, Pearson, 2021.

Teaching methods

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

Assessment methods

During the course, students will be provided with case studies from selected scientific articles or/and available dataset and will be required to write an essay dealing with the data analysis and interpretation.

For those students who do not deliver the essay, or fail, it will be possible to take a written exam with multiple-choice and open-ended questions on the foundations of data analysis and on the interpretation of a case study.

Teaching tools

Additional materials (as scientific papers, slides, data..) will be provided during the lessons and the e-learning platform will enable access to this contents.

Office hours

See the website of Meri Raggi

See the website of Yari Vecchio


Quality education

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.