MSE-213 / 3 credits

Teacher: Moll Philip Johannes Walter

Language: English


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.

Content

Keywords

Statistics, Probability, big data, experimental design, R

Learning Prerequisites

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.

Assessment methods

written exam

Supervision

Assistants Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

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

Ressources en bibliothèque

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Probability and statistics for materials science
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
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