course in statistics is suggested in order to properly follow the lectures.
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 https://www.uniud.it/it/didattica/info-didattiche/regolamento-didattico-del-corso/LM-informatica/all-B2
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.
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.
1) J. Maindonald, W.J. Braun: Data Analysis and Graphics Using R – An
Example-Based Approach (Third Edition); Cambridge University Press,
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.