
Course unit
STATISTICAL MODELS 1 (Ult. numero di matricola pari)
SCP4063241, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
SECSS/01 
Statistics 
9.0 
Course unit organization
Period 
Second semester 
Year 
2nd Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Laboratory 
3.0 
22 
53.0 
2 
Lecture 
6.0 
42 
108.0 
No turn 
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)
KENNE PAGUI
EULOGE CLOVIS
(Membro Effettivo)
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:

The following previous courses are required: Mathematics, Statistics I and II, Linear algebra, Probability. 
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, lasting two ours, with three exercises aiming at assessing the statistical knowledge and analysis skills acquired throughout the class. Students are also asked to comment on R output. 
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 (6 CFU)
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 (3 CFU)
 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 (42 hours) and exercises with R (22 hours). 
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.

Innovative teaching methods: Teaching and learning strategies
 Laboratory
 Interactive lecturing
 Problem solving
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 R
Sustainable Development Goals (SDGs)

