Scheda insegnamento

Anno Accademico 2022/2023

Conoscenze e abilità da conseguire

By the end of the course, the student should know the main topics in statistical inference. In particular the student should be able to: 1- derive an estimator and its properties; 2- define and verify parametric and non parametric statistical hypothesis in simple contexts; 3- build confidence intervals; 4 – fit a simple linear regression model.


  • Random sampling and sampling distributions. Central limit theorem.
  • Estimation theory. Point estimation: finite sample and asymptotic estimator properties
  • Confidence intervals.
  • Statistical hypothesis tests and their logic and interpretation. The concept of p-values. Tests for many standard problems including the mean, proportion, two sample comparison, categorical data. Issues with and criticism of tests.


P.S. Mann "Introductiory Statistics" eight edition, Wiley 2013.

J.A. Rice "Mathematical Statistics and Data Analysis" third edition, Duxbury/Thomson/Brooks/Cole 2007.

Lecture notes.

Metodi didattici

Classroom lessons and tutorials

Modalità di verifica e valutazione dell'apprendimento

2 hours written exam with different question covering different aspects of the course including a theoretical question (understanding of the theory behind the methods, and how it can be derived), questions asking for applying methods learned in the course to data and interpretation of results (ability to apply the methods and to understand and interpret results in practically relevant ways). There will also be some yes/no questions with short reason to test the understanding of the general reasoning behind the material. Up to

30 marks can be earned in the exam. There are also 5/30 bonus marks to be added to the exam marks (as long as they are below 30) for regular homework activity (exercises have to be done even if not necessarily correctly). Normally a 30L will be achieved by achieving a sum of larger than 30 from homework bonus marks and the exam, however I may give a 30L for a truly exceptional exam performance alone.

Strumenti a supporto della didattica

Lecture notes and additional material to be posted on Virtuale.

Orario di ricevimento

Consulta il sito web di Christian Martin Hennig