Laboratory of Mathematical Statistics

A.Y. 2020/2021
3
Max ECTS
36
Overall hours
SSD
MAT/06
Language
Italian
Learning objectives
Undefined
Expected learning outcomes
Undefined
Course syllabus and organization

Single session

Responsible
Lesson period
Second semester
in the case of lockdown, in addition to Teams, students are required to have statistical software installed on his/her personal PC (R). Tutorial on Ariel on how to install it.
Course syllabus
1. Introduction to statistical software (SAS or R)
2. Introduction to GLM (General Linear Models): general formulation, link function
3. Case in which the link function is the identity: linear regression model.
3.1. Parameters estimates
3.2. Methods for variable selection
3.3. Residual analysis and regression diagnostics
4. Presence of dummy variables
5. One way and two ways ANOVA
6. Link function as logit: logistic regression
7. Classification and regression trees (CART)
8. Introduction to neural networks
Prerequisites for admission
The content of the course Mathematical Statistics (ex CPSM2) is strongly recommended for an in-depth understanding
Teaching methods
computer lab lessons
Teaching Resources
· G.G. Roussas, A Course in Mathematical Statistics, Academic Press, 1997 (or more recent editions)
· A.Agresti, Categorical data analysis, Wiley, 2nd edition (2002)
· N. Draper, H. Smith, Applied Regression Analysis, Last Edition
· A.J.Izenman, Modern multivariate statistical techniques.. Regression, classification, and manifold learning, Springer, 2008
Assessment methods and Criteria
reports during the course
MAT/06 - PROBABILITY AND STATISTICS - University credits: 3
Laboratories: 36 hours
Educational website(s)
Professor(s)
Reception:
on appointment
office 2099
Reception:
Appointment by email
Office