First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
STATISTICAL MODELS 1 (Ult. numero di matricola dispari)
SCP4063241, A.A. 2018/19

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

Information on the course unit
Degree course First cycle degree in
SC2095, Degree course structure A.Y. 2014/15, A.Y. 2018/19
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL MODELS 1
Website of the academic structure
Department of reference Department of Statistical Sciences
E-Learning website
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 LAURA VENTURA SECS-S/01
Other lecturers PAOLO GIRARDI M-PSI/03

Course unit code Course unit name Teacher in charge Degree course code
SCP4063241 STATISTICAL MODELS 1 (Ult. numero di matricola dispari) LAURA VENTURA SC2094

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/01 Statistics 9.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 3.0 22 53.0 2
Lecture 6.0 42 108.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
6 Commissione a.a.2018/19 (matr.pari) 01/10/2018 30/09/2019 GRIGOLETTO MATTEO (Presidente)
VENTURA LAURA (Membro Effettivo)
5 Commissione a.a.2018/19 (matr.dispari) 01/10/2018 30/09/2019 VENTURA LAURA (Presidente)
GIRARDI PAOLO (Membro Effettivo)
GRIGOLETTO MATTEO (Membro Effettivo)

Prerequisites: Mathematics
Statistics I and II
Linear algebra
Target skills and knowledge: The course aims to introduce the students to statistical methods for the construction, validation and use of regression models.
The course also provides the tools necessary for practical analyses of regression models, using the statistical software R.
Examination methods: Written exam.
Assessment criteria: The assessment of the preparation of the the student will be based on their understanding of the arguments, on the acquisition of the concepts and methodologies proposed in the course, and the ability to apply them.
Course unit contents: The linear model

The linear regression model
- Problems
- The normal linear regression model
- Inference based on the likelihood: point estimation, confidence intervals, hypothesis testing on the linear regression coefficients and F test
- Assumptions of the second order and the Gauss Markov Theorem.
- Critical analysis and model building: diagnostic methods (residuals analysis, identification of outliers and leverage points), techniques for variable selection.

Analysis of variance and covariance
- Models with indicator covariates
- Analysis of variance
- Analysis of covariance

The generalized linear model
- Critical discussion of linear models and motivations for their generalization.
- Binary data and logistic regression models (likelihood, parameter estimation, interpretation of the parameter estimates, problems of hypothesis testing).
- The Poisson regression (likelihood, parameter estimation, hypothesis testing problems).
Planned learning activities and teaching methods: The course is based on lectures and exercises with R.
Additional notes about suggested reading: Lessons and labs are based on the textbook.
Textbooks (and optional supplementary readings)
  • Grigoletto, M., Pauli, F., Ventura, L., Modello Lineare - Teoria e Applicazioni con R. Torino: Giappichelli, 2017. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Interactive lecturing
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Le lezioni di teoria ed i laboratori sono basati sul libro di testo. Altro materiale didattico, esercizi e prove di esame degli anni precedenti sono resi disponibili nella piattaforma Moodle prima del

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