95966 - BUSINESS DATA ANALYSIS

Anno Accademico 2023/2024

  • Docente: Meri Raggi
  • Crediti formativi: 6
  • SSD: SECS-S/03
  • Lingua di insegnamento: Inglese
  • Moduli: Meri Raggi (Modulo 1) Yari Vecchio (Modulo 2)
  • Modalità didattica: Convenzionale - Lezioni in presenza (Modulo 1) Convenzionale - Lezioni in presenza (Modulo 2)
  • Campus: Bologna
  • Corso: Laurea Magistrale in Food Animal Metabolism and Management in the Circular Economy (cod. 5814)

Conoscenze e abilità da conseguire

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.

Contenuti

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

Testi/Bibliografia

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.

Metodi didattici

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

Modalità di verifica e valutazione dell'apprendimento

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.

Strumenti a supporto della didattica

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

Orario di ricevimento

Consulta il sito web di Meri Raggi

Consulta il sito web di Yari Vecchio

SDGs

Istruzione di qualità

L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.