Academic Year 2023-2024



Paolo Vidoni
Valentina Mameli
Course Year
Unit Credits
Teaching Period
First Period
Course Type
Prerequisites. No formal prerequisite is set; nevertheless, the frequency of the module of mathematics is suggested in order to properly follow the lectures.
Teaching Methods. Frontal lectures and exercises. The lab part involves the use of the R statistical software.
Verification of Learning. The exam consists of a written part and an optional oral part. The written test is divided into two parts. The first part (which defines 75% of the final grade) consists of exercises and theory questions. The second part (which defines 25% of the final grade) concerns the analysis of a dataset to be carried out using R. The written test is passed if the grade of both parties is greater than or equal to 18 and is the weighted average of the two grades. The optional oral exam can lead to an increase or decrease in the grade of the written exam.

The general evaluation criteria are available at

More Information. For each topic, concerning both theory and applications, printed slides, exercise sheets and lab exercises will be

provided. The material will be provided on the dedicated university web page (

The aim of the course is to introduce the fundamental concepts of

descriptive and inferential statistics, as basic tools for research and data

analysis. The students are expected to acquire the fundamental skills in

order to perform autonomously the analysis of real datasets, involving

the use of basic statistical procedures.

Knowledge and understanding

Knowledge and understanding of descriptive statistics and of how to summarize data, of the notion of uncertainty and of the basics in

probability theory and sampling theory, of the fundamental concepts of

inferential statistics, of the procedures for studying the relationships

between variables and of the basic elements of hypothesis testing.

Applying knowledge and understanding

Understanding of statistical methods as useful instruments for research in

data science and computer science applications and ability to use

descriptive and inferential statistics in order to summarize information, to

analyze and interpret relationships between variables and to test


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 statistical report and for applying more

advanced statistical techniques.

More information on the degree course are available on

The course introduces students to the fundamental concepts of descriptive and inferential statistics. These notions will be presented focusing also on applications, with particular regard to data science and computer science applications. These notions will be presented from an applied point of view during the lab part, based on the R statistical software.

Course contents:

1) Descriptive statistics: variables; frequency distributions; location and variability indicators; bivariate analysis.

2) Probability: basic concepts; random variables; probabilistic models; random vectors and convergence concepts; sampling theory.

3) Inference: point estimation; confidence intervals; hypothesis testing; correlation; simple linear regression models

Supplementary readings:

1) Borra, S., Di Ciaccio, A. (2021). Statistica. IV ed., Mc Graw-Hill

2) Navidi, W. (2006). Probabilità e Statistica per l’ingegneria e le scienze. McGraw-Hill

3) Iacus, S.M. (2006). Statistica. McGraw-Hill

4) Iacus, S. M., Masarotto, G. (2013). Laboratorio di statistica con R. II ed., Mc Graw-Hill

5) Crivellari, F. (2006). Analisi statistica dei dati con R. Apogeo