Laboratory of Mathematical Statistics
A.Y. 2020/2021
Learning objectives
Undefined
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
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
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
· 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
Professors:
Aletti Giacomo, Micheletti Alessandra
Professor(s)
Reception:
on appointment
office 2099