Academic Year 2023-2024



Paolo Vidoni
Unit Credits
Teaching Period
First Period
Course Type
Prerequisites. No formal prerequisite is set; nevertheless, the frequency of a basic course in statistics is suggested in order to properly follow the lectures.
Knowledge and understanding: knowledge and understanding of

univariate and multivariate descriptive statistics and of how to

summarize and visualize data, of the basics in inferential statistics, of the

fundamental elements of statistical modelling, of the basic concepts of

statistical learning, focusing on regression models and multivariate data

analysis techniques, and understanding of at least one statistical

software for data analysis and statistical learning applications.

Applying knowledge and understanding: understanding of statistical

methods as useful instruments for research in economics and social

sciences, ability to use descriptive and inferential statistics in order to summarize information, to analyze and interpret relationships between

variables and to test hypotheses, ability to use at least one statistical

software in order to develop simple data analysis.

Making judgements: making judgements on the appropriate statistical

models and methods to be used for analyzing a specific dataset and on

the interpretation of the experimental results.

Communication skills: communication skills in order to present a

statistical analysis, including both the methodology and the final

conclusions, in a consistent and convincing way.

Learning skills: learning skills based on the prerequisites that are

required for understanding autonomously a report with a statistical

analysis and for learning more advanced statistical procedures.

More information on the degree course are available on

The course focuses on statistical methods for data analysis. The aim is to introduce the fundamental elements of statistical modeling and the basic concepts of statistical learning, with particular attention to regression models and multivariate data analysis techniques. These notions will be presented from an applied point of view and part of the course will take place in the computer lab, using the R statistical software.

Course outline

1) Introduction to statistics and data analysis;

2) Explorative data analysis;

3) A review of inference concepts;

4) Linear regression with a single predictor;

5) Towards multiple linear regression and logistic regression;

6) Predictive and classification methods;

7) Unsupervised methods (principal component analysis, cluster


8) Tree-based methods for regression and classification.

Supplementary readings:

1) J. Maindonald, W.J. Braun: Data Analysis and Graphics Using R – An Example-Based Approach (Third Edition); Cambridge University Press, 2010.

2) J. Ledolter, R.V. Hogg: Applied Statistics for Engineers and Physical Scientist (Third Edition); Prentice Hall, 2009.

3) J.P. Marques de Sá: Applied Statistics Using SPSS, STATISTICA, MATLAB and R; Springer, 2007.