# DESU Magistère Ingénieur EconomisteUE Statistics

Browsing
Informations

## Content

1) Goals : This course introduces the basic concepts of inferential statistics. The key goal is to lay out the foundations for the advanced statistics/econometrics courses.

2) Course overview :

Chapter 1 : Estimation

1. Basic concepts : parameter, estimator, estimate, bias, variance, mean squared error (MSE), confidence interval
2. Examples : empirical mean/variance especially in the Gaussian case, maximum likelihood estimator (MLE), least squares estimator

Chapitre 2 : Tests

1. Basic concepts : null/alternative hypothesis, test statistic, type I and type II errors
2. Examples : tests in the Gaussian case (about the value of a mean, about the equality of means), significance of a regression coefficient

## Professional skills

The student should be able to tackle simple problems related to estimation and testing. He/she should learn the basic tools necessary to study Gaussian random samples.

## Language used

Main language used by this course: Français.

## Bibliography

Tassi P., Méthodes statistiques (3ème édition), Economica.

Monfort A., Cours de statistique mathématique (3ème édition), Economica.

## Recommended prerequisites

Basic probability theory : discrete distributions (Bernoulli, binomial, Poisson) and continuous ones (uniform, exponential, Gaussian), expectation, variance, independent random variables.

## Structure and organisation

24 hours integrated course.

## Volume of teachings

• Lectures: 24 hours

## Teacher

• Gilles STUPFLER