Academic Year 2019-2020


Paolo Vidoni
Total Course Credits: 6
Teaching Period: First Period
Teaching Language: Inglese
Prerequisites. No formal prerequisite is set; nevertheless, the frequency of a basic course in statistics is suggested in order to properly follow the lectures.
Teaching Methods. Frontal lectures and exercises. Part of the course will take place in the computer lab, using the R statistical software.
Verification of Learning. The exam consists in a written part and in an oral presentation of a written report. The written report may consist in the analysis of a real dataset, or in a presentation of a library of the R statistical software or in the presentation of a statistical procedure not considered during the course.
More Information. For each topic, printed slides, exercise sheets and lab exercises will be provided.


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.


The course focuses on statistical methods for data analysis. The aim is to introduce the fundamental elements of statistical modelling 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 analysis).


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.