
Course unit
MATHEMATICAL STATISTICS
SC01107882, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2016/17
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
MAT/06 
Probability and Mathematical Statistics 
6.0 
Course unit organization
Period 
First semester 
Year 
3rd Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Practice 
3.0 
24 
51.0 
No turn 
Lecture 
3.0 
24 
51.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
8 Statistica Matematica  a.a. 2019/2020 
01/10/2019 
30/09/2020 
FORMENTIN
MARCO
(Presidente)
COLLET
FRANCESCA
(Membro Effettivo)
BARBATO
DAVID
(Supplente)
BIANCHI
ALESSANDRA
(Supplente)
CALLEGARO
GIORGIA
(Supplente)
FISCHER
MARKUS
(Supplente)

7 Statistica Matematica  a.a. 2018/2019 
01/10/2018 
30/09/2019 
PAVON
MICHELE
(Presidente)
FORMENTIN
MARCO
(Membro Effettivo)
BARBATO
DAVID
(Supplente)
BIANCHI
ALESSANDRA
(Supplente)
CALLEGARO
GIORGIA
(Supplente)
FISCHER
MARKUS
(Supplente)

Prerequisites:

Prerequisites: Basic probability and Statistics. Fundamental notions of analysis and linear algebra. 
Target skills and knowledge:

It is expected that, at the end of the course, students are familiar with certain fields of classical statistics such as parametric estimation and hypothesis testing. In particular, they must learn some fundamental distributions such as those of exponential class. They must also be capable of applying tools and concepts of analysis and linear algebra to study such problems. 
Examination methods:

Threehour written test. 
Assessment criteria:

Evaluation will be based on the following criteria:
1. Comprehension of the covered fundamental concepts;
2. Capability of calculating closed form solutions in simple problems;
3. Thoroughness of preparation;
4. Clarity of exposition. 
Course unit contents:

This is an introductory course to the basic concepts of classical statistics from a predominantly mathematical point of view. Course program:
 Introductory notions on problems and methods of mathematical statistics;
 Statistics, sufficient statistics; exponential class distributions;
 Unbiased estimators with uniformly minimum variance;
 RaoCramer lower bound and efficient estimators;
 Linear models. Least squares principle;
 Maximum likelihood estimators;
 Test for simple alternative hypotheses; NeymanPearson test. 
Planned learning activities and teaching methods:

Frontal lectures. The theoretical part is continuously illustrated through examples. Moreover, exercises to be worked out at home are proposed for each topic. Some applications of hypothesis testing to telecommunication engineering problems are also presented. 
Textbooks (and optional supplementary readings) 

G.Andreatta e W.Runggaldier, Statistica Matematica: Problemi ed Esercizi Risolti. : Liguori Editore, 1983.

M. Pavon, Appunti di statistica matematica.. : Disponibili nel sito del corso, 2014.

Hogg, Robert V.; Craig, Allen T., Introduction to mathematical statisticsRobert V. Hogg , Allen T. Craig. New York: Macmillan, London, Collier Macmillan, 1978.

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Problem based learning
 Case study
 Questioning
 Story telling
 Problem solving
 Loading of files and pages (web pages, Moodle, ...)
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
Sustainable Development Goals (SDGs)

