# Probability and statistics II

lbir1315  2020-2021  Louvain-la-Neuve

Probability and statistics II
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
3 credits
22.5 h + 22.5 h
Q1
Teacher(s)
Language
French
Prerequisites
LBIR1212

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
Introduction to statistics - Common methods for point estimation - Confidence interval for a mean and a variance - Hypothesis testing and inference - Linear models and regression.
Aims
 At the end of this learning unit, the student is able to : 1 a.     Contribution of this activity to the learning outcomes referential : 1.1, 2.1 b.     Specific formulation of the learning outcomes for this activity A the end of this activity, the student is able to : ·       Name, describe and explain the theoretical concepts underlying the statistical inference approach and the theoretical models that are used in this framework; ·       Connect the deductive approach of probability theory to the inductive approach of statistical inference by clearly identifying the probabilistic models that are subject to this inference; ·       Translate mathematically textual statements if an inferential problem in statistics by using a rigorous mathematical and appropriate statistical models and by relying on appropriate theoretical tools and estimation methods; ·       Solve an applied problem by using a sound approach that relies on a correct use of well identified models and relevant tools of the inferential statistical framework; ·       Validate the internal consistency of the mathematical expressions and results based on data at hand and logical constraints that are induced by the statistical framework;
Content
The course will complete the basic notions already presented during the course LBIR 1212 - Probability & Statistics (I). The student will be able to use the most classical estimation and inference methods for one or two means or variances, as well as for the most classical linear models. Few exercises will be devoted to the use of computer software in order to illustrate the various concepts.
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Regular courses and supervised practical exercises
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Evaluation: Open book written examination (only with the original material). The examination is composed of exercises to be solved. Its duration is about 3 hours.
Other information
The course does not require specific material that would be considered as mandatory.
Online resources
Moodle
Faculty or entity

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master  in Data Science : Statistic

Minor in Statistics, Actuarial Sciences and Data Sciences

Bachelor in Bioengineering 