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DESU Magistère Ingénieur EconomisteUE Statistics

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

THE TRAININGS WHICH USE THIS COURSE