41301 - Statistical Inference

Course Unit Page

Academic Year 2018/2019

Learning outcomes

By the end of the course, the student should know the main topics in statistical inference. Specifically, the student should be able to: 1- derive an estimator and its properties; 2- use estimating methods; 3- define and verify parametric and non parametric statistical hypothesis in simple contexts; 4- build confidence intervals.

Course contents

Random sampling, sample space and random variables

Probability density functions of statistics

The likelihood concept

Point estimation: methods for finding and methods for evaluating estimators. Maximum likelihood and least squares estimates

Interval estimation

Testing statistical hypotheses: Fisher significance theory and Neyman-Pearson theory

Test-statistics of comparing means, proportions, variances, distributions.

Testing statistical hypotheses: Fisher significance theory and Neyman-Pearson theory

Test-statistics of comparing means, proportions, variances, distributions

Distribution free tests

Readings/Bibliography

Teacher's notes

Text book

G. Cicchitelli, Statistica: principi e metodi, Pearson, Milano, 2012

A. Montanari, P. Agati, DG.Calo, Statistica con esercizi commentati e risolti, OPEN, Masson, 1998

Teaching methods

Classroom

Lab classes

Assessment methods

Written examen including 5-6 numerical and therical exercises

Oral examen about all the topics of the program with particular attention on the results of written examen

Teaching tools

Teacher's notes

Text books

Lab classes

Office hours

See the website of Paola Monari

See the website of Patrizia Agati