Organizzazione della Didattica


Data analysis


Corsi comuni


Frontali Esercizi Laboratorio Studio Individuale
ORE: 64 0 0 69


I anno1 semestre







Calendario Attività Didattiche



affine/integrativo Nessun ambitoSECS-S/019

Responsabile Insegnamento

Prof.ssa CHIOGNA MONICASECS-S/01Dipartimento di Scienze Statistiche

Altri Docenti

Non previsti

Attività di Supporto alla Didattica

Non previste


Basic Mathematics (undergraduate level). It would be advantageous to have some background knowledge of elementary Probability Theory.

The course is organized into two distinct parts, i.e., Statistical Methods and Applied Multivariate Techniques. The purpose of the first part is to give an introduction to the statistical method and to related concepts. Lectures will present the tools and concepts of statistical data analysis routinely used in a variety of disciplines such as agriculture, medicine, biological sciences, economics, engineering and so on. Such modulus is open to University of Padova PhD students and to Unipd Master students coming from the School of Science and of Engineering. Applied Multivariate Techniques provides a quick overview of multivariate techniques.

Lectures and Laboratories

Part 1: Statistical Methods (6CFU) - Visualization: plots including histograms, box plots, scatterplots, scatterplot matrices, etc. - Summary statistics and goodness-of-fit tests. One- and two-sample examples, t and F distributions. - Concepts of simulation: simple simulation experiments. - Linear regression, including multiple linear regression. Associated inference problems. Regression diagnostics. Classical approaches to ANOVA. Model selection. - Logistic regression and Poisson regression. - Introduction to the design of experiments, observational studies and sampling methods. Part 2: Applied Multivariate Techniques (3CFU) - Dimension reduction - Classification - Clustering

For PhD students taking the first part of the course, the exam consists in the preparation of a poster, to be presented at a poster session organized at the end of the course. Other students i will take a written exam, comprehensive of both parts of the course.

The successful student should show essential knowledge of the key concepts, development of skills in the analysis of data and competency in applications.

Nolan, D.A. & Speed, T., Stat Labs: Mathematical Statistics Through Applications. : Springer, 2000 Venables, W.N. & Ripley, B.D., Modern Applied Statistics with S.. : Springer, 2002 Härdle, W. Simar, L., Applied Multivariate Statistical Analysis. : Springer, 2007 Mardia, K.V., Kent, J.T., and Bibby, J.M., Multivariate Analysis. : Academic Press, 1979 Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. : Springer, 2001 Venables, W.N. & Ripley, B.D., Modern Applied Statistics with S. : Springer, 2002

Specific applications can be found in the following books: -Campbell, R.C. (1989). Statistics for Biologists (3rd ed.). Cambridge University Press. -Devore, J.L. (2000). Probability and Statistics for Engineering and the Sciences (5th ed.). Duxbury Press, Pacific Grove, CA. -Agresti, A. & Finlay. B. (2007). Statistical Methods for the Social Sciences (4th ed.). Prentice Hall