First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP4063744, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
SC2094, Degree course structure A.Y. 2014/15, A.Y. 2019/20
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL MODELS 2
Website of the academic structure
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge ALESSANDRA SALVAN SECS-S/01
Other lecturers MANUELA CATTELAN SECS-S/01

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 9.0

Course unit organization
Period First semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 2.0 16 34.0 2
Lecture 7.0 48 127.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
2 Commissione a.a.2019/20 01/10/2019 30/09/2020 SALVAN ALESSANDRA (Presidente)
CATTELAN MANUELA (Membro Effettivo)
MENARDI GIOVANNA (Membro Effettivo)

Prerequisites: Students are assumed to have background knowledge of the following topics covered in undergraduate courses of the Department of Statistical Sciences.
Introduction to Probability
Linear Algebra
Statistics 1 and 2
Statistical Models 1
Target skills and knowledge: The course aims to expand the knowledge of regression models, in particular introducing the students to the theory and applications of generalized linear models. These include models for continuous, binary, categorical, count data. The course also provides the tools necessary for data analysis usig generalized linear models, using the statistical software R.
The students are expected to gain the following skills:
1. Knowledge of the methodology for the specification, inference and evaluation of the various models;
2. Ability to analyze a data set, even with a certain degree of complexity, by specifying, fitting and critically evaluating possible models.
Examination methods: Written exam in computer lab (answer to some exam questions will require the use of the software R).
Detailed exam rules, as well as sample exams with solutions are available at the Moodle page of the course (
Assessment criteria: Student’s assessment will consider how the topics presented are mastered in problems and applications.
Course unit contents: Generalized linear models (GLM)
- Exponetial families, exponential dispersion families and GLM's: models, moments, link function, likelihood.
- Inference on the parameters of a GLM (point and interval estimation, hypothesis testing).
- Model adequacy: deviance and residuals. Model selection.
- Models for binary data.
- Multinomial models for nominal and ordinal responses.
- Models for count data: sampling schemes, Poisson regression, contingency tables and log linear models.
- Overdispersion with binary and count data: diagnosis, mixture models, beta-binomial and negative binomial regression.
- Models for zero-inflated count data.
- Inference based on estimating equations and quasi-likelihood.
- Models for correlated responses: marginal models, multivariate normal responses, generalized estimating equations, generalized linear mixed models.
Planned learning activities and teaching methods: Lectures (48 hours) and Computer Lab (16 hours) classes.
Additional notes about suggested reading: Lecture notes (including material for Labs) are available on the course website.
Further references are listed below.
Textbooks (and optional supplementary readings)
  • Agresti, A., Foundations of Linear and Generalized Linear Models. Hoboken: John Wiley & Sons Inc, 2015. Cerca nel catalogo
  • Dobson, A. and Barnett, A., An Introduction to Generalized Linear Models, Third Edition. Boca Raton, FL: Chapman and Hall/CRC, 2008.
  • Madsen, H. and Thyregod, P., Introduction to General and Generalized Linear Models. Boca Raton, FL: Chapman and Hall/CRC, 2010. Cerca nel catalogo
  • Azzalini, A., Inferenza Statistica: una Presentazione basata sul Concetto di Verosimiglianza. Milano: Springer-Italia, 2001. Capitolo 6 (in English: Azzalini, A. (1996). Statistical Inference, based on the Likelihood. Chapman and Hall, Chapter 6) Cerca nel catalogo
  • Pace, L., Salvan, A., Introduzione alla Statistica - II. Inferenza, Verosimiglianza, Modelli. Padova: Cedam, 2001. Capitoli 8 e 10 Cerca nel catalogo
  • Bortot, P., Ventura, L., Salvan, A., Inferenza Statistica: Applicazioni con S-Plus e R. Padova: Cedam, 2000. Capitolo 5 Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • visual presenter

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