MSE-213 / 3 crédits

Enseignant: Moll Philip Johannes Walter

Langue: Anglais

## Summary

The students understand elementary concepts of statistical methods, including standard statistical tests, regression analysis and experimental design. They apply computational statistical methods to analyse larger data sets.

## Keywords

Statistics, Probability, big data, experimental design, R

## Important concepts to start the course

• Basic concepts of programming
• Basic calculus and matrix calculations

## Learning Outcomes

By the end of the course, the student must be able to:

• Examine the conclusions of a given statistical analysis.
• Use the method of least squares
• Define random variables, probability distributions, the central limit theorem and the law of big numbers.
• Analyze a population according to the ANOVA method.
• Perform a Student Test.
• Implement statistical methods computationally using R-code.

## Transversal skills

• Take account of the social and human dimensions of the engineering profession.
• Access and evaluate appropriate sources of information.

## Teaching methods

Lectures combined with exercises to solve computational examples.

## Expected student activities

Attendance of lectures and solving of exercises on the computer. A laptop computer will be required for this course.

written exam

## Supervision

 Assistants Yes

No

## Bibliography

Introduction to Statistics and Data Analysis, Christian Heumann and Michael Schomaker Shalabh, Springer

## Dans les plans d'études

• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Probability and statistics for materials science
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 1 Heure(s) hebdo x 14 semaines

## Semaine de référence

 Lu Ma Me Je Ve 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22