
Course unit
STATISTICAL MODELS 2
SCP4063744, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Educational activities in elective or integrative disciplines 
SECSS/01 
Statistics 
9.0 
Course unit organization
Period 
First semester 
Year 
3rd Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Laboratory 
2.0 
16 
34.0 
2 
Lecture 
7.0 
48 
127.0 
No turn 
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.
Calculus
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 (https://elearning.unipd.it/stat/). 
Assessment criteria:

Studentâ€™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, betabinomial and negative binomial regression.
 Models for zeroinflated count data.
 Inference based on estimating equations and quasilikelihood.
 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.

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.

Azzalini, A., Inferenza Statistica: una Presentazione basata sul Concetto di Verosimiglianza. Milano: SpringerItalia, 2001. Capitolo 6 (in English: Azzalini, A. (1996). Statistical Inference, based on the Likelihood. Chapman and Hall, Chapter 6)

Pace, L., Salvan, A., Introduzione alla Statistica  II. Inferenza, Verosimiglianza, Modelli. Padova: Cedam, 2001. Capitoli 8 e 10

Bortot, P., Ventura, L., Salvan, A., Inferenza Statistica: Applicazioni con SPlus e R. Padova: Cedam, 2000. Capitolo 5

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
Sustainable Development Goals (SDGs)

