Statistical quality control.

lstat2310  2019-2020  Louvain-la-Neuve

Statistical quality control.
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
4 credits
15.0 h + 5.0 h
Francq Bernard (compensates Govaerts Bernadette); Govaerts Bernadette;
Main themes
- Statistical tools for quality insurance - Principles and classes of Shewhart control charts - CUSUM and EWMA control charts - Control charts for autocorrelated and multivariate data - Capability analysis - Decomposition of sources of variability. Gauge analysis. - Reception sampling

At the end of this learning unit, the student is able to :

1 At the end of this course, the students will have gain knowledge and a critical view of the statistical tools usefull in the setup of quality insurance policy, in process control and daily follow up of analytical devices. They will be able to apply these tools to industrial data sets.

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
The themes discussed in this course are :
  • Statistical tools for quality insurance
  • Principles and classes of Shewhart control charts
  • CUSUM and EWMA control charts
  • Control charts for autocorrelated, multivariate and short run data
  • Capability analysis
  • Reception sampling
Teaching methods
Lectures (15h)
  • Methods presentation on the basis of real-life situations.
  • Formal but intuitive discussion of theoretical concepts and formulae for most methods.
  • Interpretation of software outputs.
  • Interactive lectures: students are encouraged to participate during the course.
 Computer labs (5h)
  • Case studies on JMP, methodological exercises, and JMP Output interpretation. 
Evaluation methods
The evaluation is based on a project, a written exam and an oral exam.  
Other information
Prerequisite : First course in statistical inference
Online resources
See the Moodle site:
 D. C. Montgomery, Statistical Quality Control. New York: Wiley.
Faculty or entity

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

Title of the programme
Master [120] in Biomedical Engineering

Master [120] in Mathematical Engineering

Master [120] in Statistic: Biostatistics

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Statistic: General

Approfondissement en statistique et sciences des données

Minor in Statistics, Actuarial Sciences and Data Sciences